Patient management systems and adherence recommendation methods

ABSTRACT

Infusion devices and related medical devices, patient data management systems, and methods are provided for monitoring a physiological condition of a patient. An exemplary method involves obtaining measurement data pertaining to the physiological condition of the patient, obtaining medical record data associated with the patient, obtaining a plurality of different adherence models associated with a plurality of different therapy regimens, wherein each adherence model of the plurality of different adherence models corresponds to a respective therapy regimen of a plurality of different therapy regimens, determining a plurality of adherence metric values associated with the patient for the plurality of different therapy regimens based on the measurement data and the medical record data using the plurality of different adherence models, and providing an indication of a recommended therapy regimen for the patient based at least in part on a respective adherence metric value associated with the recommended therapy regimen.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application claims the benefit of the following United StatesProvisional patent applications: U.S. Provisional Patent ApplicationSer. No. 62/476,444, filed Mar. 24, 2017; U.S. Provisional PatentApplication Ser. No. 62/476,451, filed Mar. 24, 2017; U.S. ProvisionalPatent Application Ser. No. 62/476,456, filed Mar. 24, 2017; U.S.Provisional Patent Application Ser. No. 62/476,468, filed Mar. 24, 2017;U.S. Provisional Patent Application Ser. No. 62/476,493, filed Mar. 24,2017; U.S. Provisional Patent Application Ser. No. 62/476,506, filedMar. 24, 2017; U.S. Provisional Patent Application Ser. No. 62/476,517,filed Mar. 24, 2017; and U.S. Provisional Patent Application Ser. No.62/534,051, filed Jul. 18, 2017, each of which is incorporated byreference herein in its entirety.

TECHNICAL FIELD

Embodiments of the subject matter described herein relate generally tomedical devices and related patient monitoring systems, and moreparticularly, embodiments of the subject matter relate to databasesystems facilitating improved patient-specific queries, predictions, andrecommendations.

BACKGROUND

Infusion pump devices and systems are relatively well known in themedical arts, for use in delivering or dispensing an agent, such asinsulin or another prescribed medication, to a patient. Use of infusionpump therapy has been increasing, especially for delivering insulin fordiabetics. Continuous insulin infusion provides greater control of adiabetic's condition, and hence, control schemes are being developedthat allow insulin infusion pumps to monitor and regulate a patient'sblood glucose level in a substantially continuous and autonomous manner,for example, overnight while the patient is sleeping.

Regulating blood glucose level is complicated by variations in theresponse time for the type of insulin being used along with eachpatient's individual insulin response. Furthermore, a patient's dailyactivities and experiences may cause that patient's insulin response tovary throughout the course of a day or from one day to the next.Accordingly, there is a need to facilitate improved glucose control thataccounts for the numerous different variables in a personalized manner.Moreover, the effects and efficacy of different therapy regimen may varyfrom one patient to the next. Thus, it is also desirable to provide abetter understanding of how an individual patient's condition is likelyto be affected by various actions, or how different therapies or actionscould improve regulation of the patient's condition. Other desirablefeatures and characteristics of the methods, devices and systemsdescribed herein will become apparent from the subsequent detaileddescription and the appended claims, taken in conjunction with theaccompanying drawings and the preceding background.

BRIEF SUMMARY

Infusion devices and related medical devices, patient data managementsystems, and methods are provided for monitoring a physiologicalcondition of a patient. One embodiment of a method of monitoring aphysiological condition of a patient involves obtaining, at a computingdevice, current measurement data for the physiological condition of thepatient provided by a sensing arrangement, obtaining, at the computingdevice, a user input indicative of one or more future events associatedwith the patient, and in response to the user input, determining aprediction of the physiological condition of the patient in the futurebased at least in part on the current measurement data and the one ormore future events using one or more prediction models associated withthe patient, and displaying, at the computing device, a graphicalrepresentation of the prediction on a display device.

In another embodiment, an apparatus of an electronic device is provided.The electronic device includes a communications interface to receivecurrent measurement data for a physiological condition of a patient froma sensing arrangement, a display device having a graphical userinterface display presented thereon, a user interface to obtain a userinput indicative of one or more future events, and a control systemcoupled to the communications interface, the display device, and theuser interface to determine a prediction of the physiological conditionof the patient in the future based at least in part on the currentmeasurement data and the one or more future events using one or moreprediction models associated with the patient, and display a graphicalrepresentation of the prediction within the graphical user interfacedisplay on the display device.

In another embodiment, a method of monitoring a glucose level of apatient is provided. The method involves obtaining, at a computingdevice from a glucose sensing arrangement, current sensor glucosemeasurement data for the patient, obtaining, at the computing device viaa user interface, a user input indicative of one or more future eventsfor the patient, determining, at the computing device, a simulatedglucose level in the future for the patient based at least in part onthe current sensor glucose measurement data and the one or more futureevents using a plurality of different prediction models associated withthe patient, wherein the plurality of different prediction modelsincludes an hourly forecasting model associated with the patient, anddisplaying, on a display device associated with the computing device, agraphical representation of the simulated glucose level with respect totime in the future.

In another embodiment, a method of monitoring a physiological conditionof a patient involves obtaining, from a sensing arrangement, currentmeasurement data for the physiological condition of the patient,predicting one or more events likely influence the physiologicalcondition of the patient at one or more different times in the futurebased at least in part on historical event data associated with thepatient, determining a plurality of forecast values for thephysiological condition of the patient associated with a plurality ofdifferent time periods in the future based at least in part on thecurrent measurement data and the one or more events using a forecastingmodel associated with the patient, and displaying, on a display device,the plurality of forecast values with respect to the plurality ofdifferent time periods in the future.

In another embodiment, a system is provided that includes a displaydevice, a sensing arrangement to obtain current measurement data for aphysiological condition of a patient, and a control system coupled tothe display device and the sensing arrangement to determine a pluralityof forecast hourly average values for the physiological condition of thepatient in the future based at least in part on the current measurementdata using an hourly forecasting model associated with the patient anddisplay the plurality of forecast hourly average values on the displaydevice.

In another embodiment, a method of monitoring a glucose level of apatient is provided. The method involves determining an hourlyforecasting model for the patient based at least in part on arelationship between historical glucose measurement data for the patientand historical event data associated with the patient, obtaining, from aglucose sensing arrangement, current glucose measurement data for thepatient, predicting one or more events likely influence the glucoselevel of the patient at one or more different times in the future basedat least in part on the historical event data associated with thepatient, determining a plurality of hourly forecast average glucosevalues for the patient in the future based at least in part on thecurrent glucose measurement data and the one or more events using thehourly forecasting model associated with the patient, and displaying, ona display device, a graphical representation of the plurality of hourlyforecast average glucose values in the future.

In another embodiment, a method of monitoring a physiological conditionof a patient involves obtaining, from a sensing arrangement, currentmeasurement data for the physiological condition of the patient,determining a first plurality of predicted values for the physiologicalcondition of the patient in the future based at least in part on thecurrent measurement data using a first prediction model, determining asecond plurality of predicted values for the physiological condition ofthe patient in the future based at least in part on the currentmeasurement data using a second prediction model different from thefirst prediction model, determining an ensemble prediction for thephysiological condition of the patient with respect to time in thefuture based at least in part on the first plurality of predictedvalues, the second plurality of predicted values, and weighting factorsassociated with the respective first and second prediction models,wherein the weighting factors vary with respect to the time in thefuture based on a relationship between a first reliability metricassociated with the first prediction model and a second reliabilitymetric associated with the second prediction model, and displaying, on adisplay device, a graphical indication of the ensemble prediction forthe physiological condition of the patient with respect to the time inthe future.

Another method of monitoring a physiological condition of a patientinvolves obtaining, from a sensing arrangement, current measurement datafor the physiological condition of the patient, determining a pluralityof predicted values indicative of the physiological condition for a timein the future based at least in part on the current measurement datausing a plurality of different prediction models associated with thepatient, wherein each predicted value of the plurality of predictedvalues is associated with a respective prediction model of the pluralityof different prediction models, determining, for each respectiveprediction model of the plurality of different prediction models, areliability metric associated with the respective prediction model basedat least in part on a relationship between the time in the future and acurrent time of day, determining, for each respective prediction modelof the plurality of different prediction models, a weighting factorassociated with the respective prediction model based at least in parton the reliability metric associated with the respective predictionmodel, determining an ensemble predicted value for the physiologicalcondition of the patient as a weighted average of the respectivepredicted values of the plurality of predicted values and the weightingfactors associated with the respective prediction models, and displayinga graphical indication of the ensemble predicted value for thephysiological condition of the patient in association with the time inthe future.

In another embodiment, an apparatus for an electronic device isprovided. The electronic device includes a communications interface toreceive current measurement data for a physiological condition of apatient from a sensing arrangement, a display device having a graphicaluser interface display including a graphical representation of thecurrent measurement data, a user interface to obtain a user inputadjusting the graphical user interface display to view into the future,and a control system coupled to the communications interface, thedisplay device, and the user interface to determine a first plurality ofpredicted values for the physiological condition of the patient in thefuture based at least in part on the current measurement data using afirst prediction model, determine a second plurality of predicted valuesfor the physiological condition of the patient in the future based atleast in part on the current measurement data using a second predictionmodel different from the first prediction model, determine an ensembleprediction for the physiological condition of the patient with respectto time in the future based at least in part on the first plurality ofpredicted values and the second plurality of predicted values, anddisplay a graphical representation of the ensemble prediction on thegraphical user interface display in response to the user input.

In another embodiment, a database system is provided. The databasesystem includes a database to maintain data pertaining to a plurality ofentities and a computing device coupled to the database to identifyrelationships between different pairs of the plurality of entities,generate metadata defining a graph structure maintaining therelationships between the different entities of the plurality ofentities for a plurality of different logical layers, and store themetadata in the database.

In another embodiment, a method of managing a database maintaining datapertaining to a plurality of patients is provided. The method involvesanalyzing, by a computing device, a graph data structure for a logicallayer in the database, the graph data structure being defined bymetadata in the database and the graph data structure comprising aplurality of entities, wherein each entity of the plurality of entitiesmaintains a logical relationship with one or more fields ofobservational data associated with a respective patient of the pluralityof patients, identifying, by the computing device, a relationshipbetween a pair of entities of the plurality of entities within thelogical layer, and updating, by the computing device, the metadata inthe database to create a link between the pair of entities.

In another embodiment, a system involves a plurality of medical devicesto obtain observational data pertaining to a plurality of patients, adatabase to maintain data pertaining to a plurality of entities, whereineach entity of the plurality of entities maintains a logicalrelationship between one or more fields of the observational data storedin the database, and a computing device coupled to the database toidentify relationships between different entities of the plurality ofentities, generate metadata defining a graph structure maintaining therelationships between the different entities of the plurality ofentities for a plurality of different logical layers, and store themetadata in the database.

In another embodiment, a method of querying a database is provided. Themethod involves receiving, by a computing device coupled to thedatabase, an input query from a client device, identifying, by thecomputing device, a logical layer of a plurality of different logicallayers of the database for searching based at least in part on the inputquery, generating, by the computing device, a query statement forsearching the identified logical layer of the plurality of differentlogical layers of the database based at least in part on the inputquery, querying the identified logical layer of the database using thequery statement to obtain result data, and providing, by the computingdevice to the client device, a search result influenced by the resultdata.

In another embodiment, a database system is provided. The databasesystem includes a database to maintain data pertaining to a plurality ofentities and metadata defining a graph structure maintainingrelationships between different entities of the plurality of entitiesfor each of a plurality of different logical layers, a client devicecoupled to a network to transmit a conversational input from a user ofthe client device, and a computing device coupled to the database andthe network to receive the conversational input from the client device,determines a logical layer of the plurality of different logical layersof the database for searching based at least in part on theconversational input, generate a query statement for searching theidentified logical layer of the plurality of different logical layers ofthe database based at least in part on the conversational input toobtain result data from the logical layer of the database, and provide asearch result influenced by the result data to the client device overthe network.

In another embodiment, a method of querying a database involvesproviding, at a client electronic device, a graphical user interfacedisplay prompting conversational interaction by a user, receiving, atthe client electronic device, a conversational input from the user,communicating, from the client electronic device to a remote device overa network, the conversational input, wherein the remote device analyzesthe conversational input to identify a logical layer of a plurality ofdifferent logical layers of the database for searching based at least inpart on the conversational input and queries the identified logicallayer of the database to obtain result data, and providing, at theclient electronic device, a conversational search result within thegraphical user interface display responsive to the conversational input,wherein the conversational search result is influenced by the resultdata.

In another embodiment, an apparatus for an infusion device is provided.The infusion device includes an actuation arrangement operable todeliver fluid to a user, the fluid influencing a physiological conditionof the user, a communications interface to receive measurement dataindicative of the physiological condition of the user, a sensingarrangement to obtain contextual measurement data, and a control systemcoupled to the actuation arrangement, the communications interface andthe sensing arrangement to determine a command for autonomouslyoperating the actuation arrangement in a manner that is influenced bythe measurement data and the contextual measurement data andautonomously operate the actuation arrangement in accordance with thecommand to deliver the fluid to the user.

In another embodiment, a method of operating an infusion device toregulate a physiological condition of a patient is provided. The methodinvolves obtaining, at the infusion device, measurement data indicativeof the physiological condition from a first sensing arrangement,determining, at the infusion device, a delivery command for autonomouslyoperating an actuation arrangement of the infusion device to deliverfluid influencing the physiological condition to the patient, obtaining,at the infusion device, contextual measurement data from a secondsensing arrangement of the infusion device, adjusting the deliverycommand in a manner that is influenced by the contextual measurementdata to obtain an adjusted delivery command, and autonomously operatingthe actuation arrangement to deliver the fluid in accordance with theadjusted delivery command.

In another embodiment, a method of monitoring a physiological conditionof a patient involves obtaining, by a computing device, measurement datapertaining to the physiological condition of the patient from a sensingarrangement, obtaining, by the computing device, medical record dataassociated with the patient from a database, determining, by thecomputing device, a risk score associated with the patient for a medicalcondition based at least in part on the measurement data, the medicalrecord data, and one or more relationships between populationmeasurement data and population medical record data, and initiating oneor more actions at the computing device based at least in part on therisk score.

In another embodiment, a system is provided that includes a sensingarrangement to obtain measurement data pertaining to a physiologicalcondition of a patient, a database to maintain medical record dataassociated with the patient, population measurement data associated witha plurality of patients, and population medical record data associatedwith the plurality of patients, and a computing device communicativelycoupled to the sensing arrangement and the database to determine a riskscore associated with the patient for a medical condition based at leastin part on the measurement data, the medical record data, and one ormore relationships between the population measurement data and thepopulation medical record data and perform one or more actions when therisk score is greater than a threshold.

In another embodiment, a method of monitoring a patient involvesobtaining, from a database, population glucose measurement dataassociated with a plurality of patients, obtaining, from the database,population medical record data associated with the plurality ofpatients, determining a risk model for a medical condition based onrelationships between population glucose measurement data and populationmedical record data for a subset of the plurality of patients having themedical condition, obtaining, from an interstitial glucose sensingarrangement, sensor glucose measurement data for the patient, obtaining,from the database, medical record data associated with the patient,determining a risk score associated with the patient for the medicalcondition based at least in part on the sensor glucose measurement dataand the medical record data using the risk model, and generating atherapy recommendation for the patient when the risk score is greaterthan a threshold.

In another embodiment, a method of managing a physiological condition ofa patient involves obtaining, by a computing device, measurement datapertaining to the physiological condition of the patient from a sensingarrangement, obtaining, by the computing device, medical records dataassociated with the patient from a database, classifying, by thecomputing device, the patient into a patient group based at least inpart on the measurement data and the medical records data, obtaining, bythe computing device, a plurality of different uplift models associatedwith the patient group, wherein each uplift model of the plurality ofdifferent uplift models corresponds to a respective therapy interventionof a plurality of different therapy interventions, determining, by thecomputing device, a plurality of uplift metric values associated withthe patient for the plurality of different therapy interventions basedon the measurement data and the medical record data using the pluralityof different uplift models, and providing, by the computing device, anindication of a recommended therapy intervention for the patient basedat least in part on a respective uplift metric value associated with therecommended therapy intervention.

In another embodiment, a method of managing a physiological condition ofa patient involves obtaining, by a computing device coupled to adatabase, population measurement data associated with a plurality ofpatients from the database, obtaining, by the computing device,population medical record data associated with the plurality of patientsfrom the database, determining, by the computing device, a patient groupcomprising a subset of the plurality of patients for modeling based onat least one of a subset of the population measurement data associatedwith the subset of the plurality of patients and a subset of thepopulation medical record data associated with the subset of theplurality of patients, determining, by the computing device, a pluralityof different uplift models associated with the patient group fordifferent therapy interventions based at least in part on one or morerelationships between the subset of the population measurement data andthe subset of the population medical record data associated with thesubset of patients, obtaining measurement data pertaining to thephysiological condition of the patient from a sensing arrangement,obtaining medical record data associated with the patient from thedatabase, determining a plurality of uplift metric values associatedwith the patient based on the measurement data and the medical recorddata using the plurality of different uplift models, selecting arecommended therapy intervention of the different therapy interventionsbased at least in part on the plurality of uplift metric values, andproviding an indication of the recommended therapy intervention for thepatient.

In another embodiment, a system is provided that includes a sensingarrangement to obtain measurement data pertaining to a physiologicalcondition of a patient, a database to maintain medical record dataassociated with the patient and a plurality of different uplift modelsassociated with a patient group based on relationships betweenpopulation measurement data and population medical record dataassociated with a plurality of patients, wherein each uplift model ofthe plurality of different uplift models corresponds to a respectivetherapy intervention of a plurality of different therapy interventions,and a computing device communicatively coupled to the sensingarrangement and the database to classify the patient into the patientgroup based at least in part on the measurement data and the medicalrecords data, determine a plurality of uplift metric values associatedwith the patient for the plurality of different therapy interventionsbased on the measurement data and the medical record data using theplurality of different uplift models, and generate a user notificationof a recommended therapy intervention for the patient based at least inpart on a respective uplift metric value associated with the recommendedtherapy intervention.

In another embodiment, a method of managing a physiological condition ofa patient involves obtaining, by a computing device, measurement datapertaining to the physiological condition of the patient from a sensingarrangement, obtaining, by the computing device, medical records dataassociated with the patient from a database, obtaining, by the computingdevice, a plurality of different adherence models associated with aplurality of different therapy regimens, wherein each adherence model ofthe plurality of different adherence models corresponds to a respectivetherapy regimen of a plurality of different therapy regimens,determining, by the computing device, a plurality of adherence metricvalues associated with the patient for the plurality of differenttherapy regimens based on the measurement data and the medical recorddata using the plurality of different adherence models, and providing,by the computing device, an indication of a recommended therapy regimenfor the patient based at least in part on a respective adherence metricvalue associated with the recommended therapy regimen.

In another embodiment, a method of managing a physiological condition ofa patient involves obtaining, by a computing device, population medicalrecords data for a plurality of patients prescribed a therapy regimen,obtaining, by the computing device, population medical claims data forthe plurality of patients, obtaining, by the computing device,population measurement data for the plurality of patients, determining,by the computing device, an adherence model based at least in part on arelationship between the population medical claims data, the populationmedical records data, and the population measurement data, obtaining, bythe computing device, measurement data pertaining to the physiologicalcondition of the patient from a sensing arrangement, obtaining, by thecomputing device, medical records data associated with the patient fromthe database, determining, by the computing device, an adherence metricvalue for the therapy regimen for the patient based at least in part onthe measurement data and the medical records data using the adherencemodel, and recommending, by the computing device, the therapy regimenfor the patient based on the adherence metric value.

In another embodiment, a system is provided that includes a sensingarrangement to obtain measurement data pertaining to a physiologicalcondition of a patient, a database to maintain medical record dataassociated with the patient and a plurality of different adherencemodels associated with a plurality of different therapy regimens,wherein each adherence model of the plurality of different adherencemodels corresponds to a respective therapy regimen of a plurality ofdifferent therapy regimens, and a computing device communicativelycoupled to the sensing arrangement and the database to determine aplurality of adherence metric values associated with the patient for theplurality of different therapy regimens based on the measurement dataand the medical record data using the plurality of different adherencemodels and generate a user notification of a recommended therapy regimenfor the patient based at least in part on a respective adherence metricvalue associated with the recommended therapy regimen.

This summary is provided to introduce a selection of concepts in asimplified form that are further described below in the detaileddescription. This summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used as an aid in determining the scope of the claimed subjectmatter.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete understanding of the subject matter may be derived byreferring to the detailed description and claims when considered inconjunction with the following figures, wherein like reference numbersrefer to similar elements throughout the figures, which may beillustrated for simplicity and clarity and are not necessarily drawn toscale.

FIG. 1 depicts an exemplary embodiment of a patient data managementsystem;

FIG. 2 is a flow diagram of an exemplary data management processsuitable implementation in connection with the patient data managementsystem of FIG. 1 in one or more exemplary embodiments;

FIG. 3 is a flow diagram of an exemplary querying process suitableimplementation in connection with the patient data management system ofFIG. 1 in one or more exemplary embodiments;

FIG. 4 depicts an exemplary graphical user interface (GUI) displaysuitable for presentation on a client electronic device in the patientdata management system of FIG. 1 in accordance with an exemplaryembodiment of the querying process of FIG. 3;

FIG. 5 depicts an exemplary graph data structure suitable forimplementation at a logical database layer in the patient datamanagement system of FIG. 1;

FIG. 6 is a block diagram of an exemplary infusion system suitable foruse with a fluid infusion device in one or more embodiments;

FIG. 7 is a block diagram of an exemplary pump control system suitablefor use in the infusion device in the infusion system of FIG. 6 in oneor more embodiments;

FIG. 8 is a flow diagram of an exemplary forecasting process suitableimplementation in connection with a patient data management system inone or more exemplary embodiments;

FIG. 9 depicts an exemplary GUI display suitable for presentation on aclient electronic device in accordance with an exemplary embodiment ofthe forecasting process of FIG. 8;

FIG. 10 depicts a block diagram of an hourly forecasting model suitablefor use in connection with an exemplary embodiment of the forecastingprocess of FIG. 8;

FIG. 11 is a flow diagram of an exemplary ensemble prediction processsuitable implementation in connection with a patient data managementsystem in one or more exemplary embodiments;

FIG. 12 depicts an exemplary GUI display suitable for presentation on aclient electronic device in accordance with an exemplary embodiment ofthe ensemble prediction process of FIG. 11;

FIG. 13 is a flow diagram of an exemplary patient simulation processsuitable implementation in connection with a patient data managementsystem in one or more exemplary embodiments;

FIGS. 14-16 depicts exemplary GUI displays suitable for presentation ona client electronic device in accordance with various exemplaryembodiments of the patient simulation process of FIG. 13;

FIG. 17 is a flow diagram of an exemplary risk management processsuitable implementation in connection with a patient data managementsystem in one or more exemplary embodiments;

FIG. 18 is a flow diagram of an exemplary uplift recommendation processsuitable implementation in connection with a patient data managementsystem in one or more exemplary embodiments;

FIG. 19 is a flow diagram of an exemplary adherence recommendationprocess suitable implementation in connection with a patient datamanagement system in one or more exemplary embodiments;

FIG. 20 depicts an exemplary embodiment of an infusion system

FIG. 21 depicts a plan view of an exemplary embodiment of a fluidinfusion device suitable for use in the infusion system of FIG. 20;

FIG. 22 is an exploded perspective view of the fluid infusion device ofFIG. 21;

FIG. 23 is a cross-sectional view of the fluid infusion device of FIGS.21-22 as viewed along line 23-23 in FIG. 22 when assembled with areservoir inserted in the infusion device;

FIG. 24 is a block diagram of an exemplary patient monitoring system;and

FIG. 25 depicts an embodiment of a computing device of a diabetes datamanagement system suitable for use in connection with any one or more ofthe systems of FIGS. 1, 6, 20 and 24 and any one or more of theprocesses of FIGS. 2-3, 8, 11, 13 and 17-19 in accordance with one ormore embodiments.

DETAILED DESCRIPTION

The following detailed description is merely illustrative in nature andis not intended to limit the embodiments of the subject matter or theapplication and uses of such embodiments. As used herein, the word“exemplary” means “serving as an example, instance, or illustration.”Any implementation described herein as exemplary is not necessarily tobe construed as preferred or advantageous over other implementations.Furthermore, there is no intention to be bound by any expressed orimplied theory presented in the preceding technical field, background,brief summary or the following detailed description.

For purposes of explanation, the subject matter may be described hereinprimarily in the context of infusion systems and devices configured tosupport monitoring and/or regulating a glucose level in the body of theuser in a personalized and/or context-sensitive manner. That said, thesubject matter described herein is not necessarily limited to glucoseregulation or insulin infusion, and in practice, could be implemented inan equivalent manner with respect to any number of other medications,physiological conditions, and/or the like.

While the subject matter described herein can be implemented in thecontext of any electronic device, exemplary embodiments described beloware implemented in connection with medical devices, such as portableelectronic medical devices. Although many different applications arepossible, the following description may primarily focus on a fluidinfusion device (or infusion pump) as part of an infusion systemdeployment. For the sake of brevity, conventional techniques related toinfusion system operation, insulin pump and/or infusion set operation,and other functional aspects of the systems (and the individualoperating components of the systems) may not be described in detailhere. Examples of infusion pumps may be of the type described in, butnot limited to, U.S. Pat. Nos. 4,562,751; 4,685,903; 5,080,653;5,505,709; 5,097,122; 6,485,465; 6,554,798; 6,558,320; 6,558,351;6,641,533; 6,659,980; 6,752,787; 6,817,990; 6,932,584; and 7,621,893;each of which are herein incorporated by reference. A fluid infusiondevice generally includes a motor or other actuation arrangement that isoperable to linearly displace a plunger (or stopper) of a reservoirprovided within the fluid infusion device to deliver a dosage of fluid,such as insulin, to the body of a user. In one or more exemplaryembodiments, delivery commands (or dosage commands) that governoperation of the motor are determined in a substantially autonomousmanner and on a substantially continual basis based on a differencebetween a measured value for a physiological condition in the body ofthe user and a target value using closed-loop control to regulate themeasured value to the target value.

As described in greater detail below in the context of FIGS. 1-5, in oneor more embodiments, historical observational patient data (e.g.,measurement data, insulin delivery data, event log data, contextualdata, and the like), electronic medical records data, and medicalinsurance claims data associated with a plurality of different patientsare stored or otherwise maintained in a database and organized into aplurality of different logical layers. Each logical layer has its ownassociated directed graph data structure that maintains associations orrelationships between different entities within that logical layer. Inthis regard, an entity generally represents a container or logicalgrouping of fields, attributes or other information characterizing theentity. Thus, an entity may maintain a logical association between oneor more fields of a patient's historical observational data, thepatient's electronic medical records data and/or the patient's medicalinsurance claims data. For example, a patient identifier and one or moreadditional fields of data associated with an individual patient may bemapped to different entities within a particular logical database layer,which in turn function as nodes within the directed graph data structureassociated with that logical database layer that are linked to othernodes (or entities) within that logical database layer. Thus,similarities or commonalities between different patients or entitieswithin a logical database layer may be utilized to establish linksbetween different patients, lifestyle events, therapy regimens, patientoutcomes, and the like, which, in turn, may be utilized to provideimproved recommendations pertaining to management of a given patient'scondition or otherwise improve the control, regulation, or understandingof a given patient's condition. Similarly, in some embodiments,similarities, commonalities, or causalities may be utilized to establishlinks between an entity within one logical database layer with anotherentity in a different logical layer, thereby establishing links or edgesthat span logical database layers.

Links or edges between different nodes (or entities) may be initiallycreated when the corresponding data for an entity is loaded, created, orotherwise instantiated in the database. For example, when a new patientis introduced into the database system, a corresponding entity for thepatient may be created within a logical database layer for patients.Thereafter, the logical database layer may be searched to identify otherentities that are related to or associated with some aspect of that newentity. For example, if the new patient's entity includes an identifierfor the patient's healthcare provider, a bidirectional link may becreated to the node corresponding to an existing entity associated withthe patient's healthcare provider (which may be in the same or differentlogical layer of the database).

In one or more exemplary embodiments, for each logical database layer,the entities or nodes in the graph data structure associated with thatlayer are periodically analyzed to identify and create new causal orlogical relationships between different nodes of the graph datastructure. In one or more embodiments, a generative recurrence neuralnetwork or other machine learning or artificial intelligence techniquesmay periodically scan nodes of the graph data structure to identifycause and effect pairs and establish causality links (or edges) betweensuch nodes of the graph data structure. For example, directional linksbetween entities corresponding to different types of meals and entitiescorresponding to different types of glucose excursion events (e.g., ahyperglycemic event, a hypoglycemic event, acute diabetic ketoacidosis,and/or the like) may be created in response to a causality engineemploying machine learning identifying a causal relationship based on acommon sequence of events occurring with respect to one or morepatients. In one or more embodiments, generative recurrence neuralnetwork techniques are applied by randomly starting from differentoutcome nodes of interest and backtracking links or edges to that nodein a “rule-less” manner to establish whether specific patterns orsequences lead to that particular outcome node. Additionally, query logsassociated with queries executed on or at a particular logical databaselayer may be analyzed to detect repeated associations or query pathsinvolving at least a threshold number of nodes to establish new edgesbetween end nodes of the query paths to improve query performance. Insome embodiments, new edges or relationships between entities or nodesin the graph data structure may also be established manually (e.g.,based upon new research, clinical evidence, data scraping and manualverification, or other external knowledge).

As described in greater detail below in the context of FIGS. 3-5, thedifferent logical database layers allow for the observational patientdata, electronic medical records data, and medical insurance claims datato be effectively translated into different forms with differentinterrelationships between different subsets of data, therebyaccommodating different types of queries. Moreover, the query resultsmay be more personalized or otherwise yield a better patient outcome,recommendation, or understanding of a patient's physiological condition.For example, natural language processing or other artificialintelligence techniques may be applied to an input query or searchstring to determine an intent or objective associated with the inputquery, and based thereon, identify one or more of the logical databaselayers for searching based on the intent of the query. Query statementsare then constructed and executed on the identified logical databaselayers to obtain results for the input query. In one or moreembodiments, the initial query results are filtered or otherwise parsedbased on information pertaining to a current operational context (e.g.,time of day, day of week, geographic location, environmental conditions,and/or the like) to obtain context-sensitive query results, which arethen output or otherwise provided in response to the input query.

As described in greater detail below primarily in the context of FIGS.3-7, In one or more embodiments, a medical device, such as an infusiondevice, a sensing device, a monitoring device, or the like, includes orotherwise supports a user interface capable of receiving aconversational input query, which, in turn, is parsed or otherwiseanalyzed at the medical device to obtain the input query to be analyzedfor purposes of identifying logical database layers for searching andgenerating corresponding query statements. For example, in one or moreembodiments, a medical device includes a microphone or similar audioinput device that is adapted to receive an audio input from a user,which, in turn is processed, parsed, or otherwise analyzed to identify aconversational input query within the audio input. The query results maysubsequently be presented or otherwise provided to the user in aconversational manner or otherwise within the context of a conversationor dialog with the user within the user interface. Thus, a patient oruser may be capable of conversationally interacting with and queryingthe database system, which, in turn, is capable of being transformed toallow the queries to be executed on different logical layers in anexpeditious manner and provide results that are personalized andcontext-sensitive while also leveraging interrelationships acrossdifferent types and subsets of data (e.g., different patients withsimilar demographic characteristics, different patients with similarmedical histories, different patients with similar therapy regimen,and/or the like).

Diabetes Intelligence Network

FIG. 1 depicts an exemplary embodiment of a patient data managementsystem 100 that includes, without limitation, a computing device 102coupled to a database 104 that is also communicatively coupled to one ormore electronic devices 106 over a communications network 108, such as,for example, the Internet, a cellular network, a wide area network(WAN), or the like. It should be appreciated that FIG. 1 depicts asimplified representation of a patient data management system 100 forpurposes of explanation and is not intended to limit the subject matterdescribed herein in any way.

In exemplary embodiments, the electronic devices 106 include one or moremedical devices, such as, for example, an infusion device, a sensingdevice, a monitoring device, and/or the like. Additionally, theelectronic devices 106 may include any number of non-medical clientelectronic devices, such as, for example, a mobile phone, a smartphone,a tablet computer, a smart watch, or other similar mobile electronicdevice, or any sort of electronic device capable of communicating withthe computing device 102 via the network 108, such as a laptop ornotebook computer, a desktop computer, or the like. One or more of theelectronic devices 106 may include or be coupled to a display device,such as a monitor, screen, or another conventional electronic display,capable of graphically presenting data and/or information pertaining tothe physiological condition of a patient. Additionally, one or more ofthe electronic devices 106 also includes or is otherwise associated witha user input device, such as a keyboard, a mouse, a touchscreen, amicrophone, or the like, capable of receiving input data and/or otherinformation from a user of the electronic device 106.

In exemplary embodiments, one or more of the electronic devices 106transmits, uploads, or otherwise provides data or information to thecomputing device 102 for processing at the computing device 102 and/orstorage in the database 104. For example, when an electronic device 106is realized as a sensing device, monitoring device, or other device thatincludes sensing element is inserted into the body of a patient orotherwise worn by the patient to obtain measurement data indicative of aphysiological condition in the body of the patient, the electronicdevice 106 may periodically upload or otherwise transmit the measurementdata to the computing device 102. In other embodiments, when theelectronic device 106 is realized as an infusion device or similardevice capable of delivering a fluid or medicament to a patient, theelectronic device 106 may periodically upload or otherwise transmitdelivery data indicating the timing and amounts of the fluid ormedicament being delivered to the patient. In yet other embodiments,client electronic device 106 may be utilized by a patient to manuallydefine, input or otherwise log meals, activities, or other eventsexperienced by the patient and then transmit, upload, or otherwiseprovide such event log data to the computing device 102.

The computing device 102 generally represents a server or other remotedevice configured to receive data or other information from theelectronic devices 106, store or otherwise manage data in the database104, and analyze or otherwise monitor data received from the electronicdevices 106 and/or stored in the database 104, as described in greaterdetail below. In practice, the computing device 102 may reside at alocation that is physically distinct and/or separate from the electronicdevices 106, such as, for example, at a facility that is owned and/oroperated by or otherwise affiliated with a manufacturer of one or moremedical devices utilized in connection with the patient data managementsystem 100. For purposes of explanation, but without limitation, thecomputing device 102 may alternatively be referred to herein as aserver, a remote server, or variants thereof. The server 102 generallyincludes a processing system and a data storage element (or memory)capable of storing programming instructions for execution by theprocessing system, that, when read and executed, cause processing systemto create, generate, or otherwise facilitate the applications orsoftware modules configured to perform or otherwise support theprocesses, tasks, operations, and/or functions described herein.Depending on the embodiment, the processing system may be implementedusing any suitable processing system and/or device, such as, forexample, one or more processors, central processing units (CPUs),controllers, microprocessors, microcontrollers, processing cores and/orother hardware computing resources configured to support the operationof the processing system described herein. Similarly, the data storageelement or memory may be realized as a random access memory (RAM), readonly memory (ROM), flash memory, magnetic or optical mass storage, orany other suitable non-transitory short or long term data storage orother computer-readable media, and/or any suitable combination thereof.

In exemplary embodiments, the database 104 is utilized to store orotherwise maintain historical observational patient data 120, electronicmedical records data 122, and medical insurance claims data 124 for aplurality of different patients. In this regard, a subset of patientshaving associated data in one of the data sets 120, 122, 124 may alsohave associated data in another one of the data sets 120, 122, 124. Thatis, some but not necessarily all of the patients having associated withone of the data sets 120, 122, 124 may be common to another of the datasets 120, 122, 124. In exemplary embodiments, the database 104 alsostores or maintains metadata 126 utilized to characterize or otherwisedefine directed graph data structures corresponding to different logicallayers within the database 104. In this regard, the graph metadata 126may define the nodes (or entities) that make up the graph data structureassociated with a particular logical database layer, with each of thosenodes (or entities) being mapped to one or more fields of the sets ofdata 120, 122, 124. Additionally, the graph metadata 126 characterizesor defines the edges or links between nodes within the graph datastructure associated with a particular logical database layer thatestablish the logical or causal relationship between nodes within thatlogical database layer. In various embodiments, a node (or entity) mayexist in multiple different logical database layers, or a node (orentity) in one logical database layer may be linked to another node (orentity) in a different logical database layer.

In the illustrated embodiment, the server 102 implements or otherwiseexecutes a data management application 110 that receives or otherwiseobtains data from the electronic devices 106, stores the received datain the database 104, generates or otherwise creates the entitieslogically associating different fields of the stored data 120, 122, 124.The data management application 110 also generates or otherwise createsthe graph metadata 126 maintaining relationships between the differententities in the database 104, as described in greater detail below inthe context of FIGS. 2-5. In the illustrated embodiment, the server 102also implements or otherwise executes a query management application 112that receives or otherwise obtains input queries from one or more of theelectronic devices 106 and generates, executes or otherwise performscorresponding query statements on one or more of the different logicallayers of the database 104 to obtain results provided to the respectiveelectronic devices 106 in response to the respective input queries, asdescribed in greater detail below in the context of FIGS. 2-5.

Still referring to FIG. 1, in exemplary embodiments, the historicalobservational data 120 maintained in the database 104 includes, inassociation with a particular patient (or patient identifier),historical measurement data indicative of the patient's physiologicalcondition (e.g., historical blood glucose values, historicalinterstitial glucose values, and/or the like) with respect to time,historical delivery data indicative of dosages of fluid or medicamentdelivered to the patient (e.g., historical meal or correction boluses,basal dosages or other automated delivery amounts, and the like) withrespect to time, historical meal data and/or other event log dataassociated with the patient, historical contextual data pertaining tothe measurement data, the delivery data, the event log data, and thelike. For example, the server 102 may receive, from a medical device viathe network 108, measurement data values associated with a particularpatient (e.g., sensor glucose measurements, acceleration measurements,and the like) that were obtained using a sensing element, and the server102 stores or otherwise maintains the historical measurement data aspatient data 120 in the database 104 in association with the patient(e.g., using one or more unique patient identifiers). Additionally, theserver 102 may also receive, from or via a client device 106, meal dataor other event log data that may be input or otherwise provided by thepatient (e.g., via a client application at the client device 106) andstore or otherwise maintain historical meal data and other historicalevent or activity data associated with the patient in the database 104.In this regard, the meal data include, for example, a time or timestampassociated with a particular meal event, a meal type or otherinformation indicative of the content or nutritional characteristics ofthe meal, and an indication of the size associated with the meal. Inexemplary embodiments, the server 102 also receives historical fluiddelivery data (e.g., insulin delivery dosage amounts and correspondingtimestamps) corresponding to basal or bolus dosages of fluid deliveredto the patient by an infusion device 106. The server 102 may alsoreceive geolocation data and potentially other contextual dataassociated with an electronic device 106 providing the patient data 120,and store or otherwise maintain the historical operational context datain association with the particular patient. In this regard, one or moreof the devices 106 may include a global positioning system (GPS)receiver or similar modules, components or circuitry capable ofoutputting or otherwise providing data characterizing the geographiclocation of the respective device 106 in real-time.

The electronic medical records (EMR) data 122 generally includes, inassociation with one or more identifiers for a given patient within theEMR data set, information indicative of medical diagnoses or medicalconditions the patient has been diagnosed with, drugs or medicationsthat have been administered or taken by the patient, prescriptioninformation, therapy changes for the patient, laboratory results ormeasurements for physiological conditions of the patient, immunizationrecords for the patient, microbiology results or other observationspertaining to the patient, healthcare utilization information (e.g.,hospitalizations, emergency room visits, outpatient visits, etc.), xdemographic information associated with the patient (e.g., age, income,education, location, gender), past medical procedures, clinicalobservations or other habitual behavior information (e.g., smoking,alcohol usage, etc.), family medical history, physician notes and careplans, and/or the like. The EMR data 122 may also include data about thehealthcare provider(s) associated with various aspects of a patient'smedical records, the patient's insurance information, and/or the like.In various embodiments, the EMR data 122 could be received or obtainedby the server 102 from another server computing device, another databasedifferent from database 104 (e.g., by replication from anotherdatabase), individual computing devices associated with healthcareproviders, patients, and/or the like. The claims data 124 generallyincludes, in association with one or more identifiers for a givenpatient within the claims data set, information pertaining to medicalinsurance claims submitted by or on behalf of the patient, includingcost information, prescriptions filled or refilled by the patient, andthe like. Similar to the EMR data 122, the claims data 124 could bereceived or obtained by the server 102 from another server computingdevice, another database different from database 104 (e.g., byreplication from another database), individual computing devicesassociated with healthcare providers, patients, pharmacies, and/or thelike. In exemplary embodiments, the claims data 124 includes medical,pharmaceutical, and confinement related claims data, including therespective diagnosis, procedure, prescription code(s), cost(s) (e.g.,net plus allowed amount(s), etc.), and the like.

FIG. 2 depicts an exemplary data management process 200 suitable forimplementation by a computing device, such as the server 102 in thepatient data management system 100 of FIG. 1. The various tasksperformed in connection with the data management process 200 may beperformed by hardware, firmware, software executed by processingcircuitry, or any combination thereof. For illustrative purposes, thefollowing description refers to elements mentioned above in connectionwith FIG. 1. In practice, portions of the data management process 200may be performed by different elements of the patient data managementsystem 100; however, for purposes of explanation, the data managementprocess 200 may be described primarily in the context of implementationat or by the server 102 and/or the data management application 110. Itshould be appreciated that the data management process 200 may includeany number of additional or alternative tasks, the tasks need not beperformed in the illustrated order and/or the tasks may be performedconcurrently, and/or the data management process 200 may be incorporatedinto a more comprehensive procedure or process having additionalfunctionality not described in detail herein. Moreover, one or more ofthe tasks shown and described in the context of FIG. 2 could be omittedfrom a practical embodiment of the data management process 200 as longas the intended overall functionality remains intact.

In exemplary embodiments, the data management process 200 is performed,facilitated, or otherwise supported by the data management application110 at the server 102 to generate graph metadata 126 for the differentlogical layers to be supported by the database 104. For example, in oneembodiment where the database system 104 maintains data 120, 122, 124pertaining to diabetic patients, the database 104 supports fivedifferent logical layers: a patient layer, a lifestyle layer, a therapylayer, a diabetes management layer, and a diabetes knowledge layer. Thepatient layer contains subsets of patient data 120 and EMR data 122pertaining to individual patients including, but not limited to,historical patient glucose measurements, information characterizinghistorical glucose excursion events, and information characterizingcomplications or improvements to a respective patient's physiologicalcondition. In this regard, the graph metadata 126 may indicate whichfields of the patient data 120 and the EMR data 122 associated with anindividual patient should be mapped to or otherwise utilized for nodesof the patient layer graph data structure along with the correspondingedges or links between those nodes. The patient layer can be queried toobtain information pertaining to the patient's health history, such asglucose measurements, excursion events, year-over-year improvements,comorbidities, complications, and/or the like. The lifestyle layerincorporates event log data and potentially other subsets of patientdata 120 and EMR data 122 pertaining to respective individual patients.The therapy layer incorporates subsets of data 120, 122, 124 thatindicate what drugs, medications, or other therapies are associated witha respective patient, and may include, for example, indication of whattypes of medical devices the patient may be using to manage or monitorhis or her therapy (e.g., an infusion device, a continuous glucosemonitoring device, or the like) along with costs associated with thepatient's therapy. The diabetes management layer incorporates subsets ofthe data 120, 122, 124 that support maintaining relationships betweendifferent individuals or entities represented within the data sets 120,122, 124 including patients, healthcare providers, physicians, payers,hospitals, and the like. The diabetes knowledge layer incorporatessubsets of the data 120, 122, 124 that supports queries for generalknowledge that is patient independent, such as, for example, queriespertaining to a particular physiological condition or diagnosis (e.g.,Type 1 diabetes, Type 2 diabetes, or the like), pharmacodynamics ofinsulin or other fluids, drugs, or medications, excursion events, typesof meals, and the like.

For each logical database layer, the illustrated data management process200 periodically scans or otherwise analyzes the nodes or entitieswithin the graph data structure associated with the respective logicaldatabase layer to identify causal relationships between entities withinthat logical database layer (tasks 202, 204). In one or more embodiment,the data management application 110 implements or otherwise performsmachine learning-based causality analysis to discover repeated cause andeffect pairings of nodes within the graph data structure. For example,timestamps or other temporal relationships between meal event entitiesand glucose excursion event entities associated with a particularpatient or across multiple different patients may be utilized toidentify a causal relationship between the meal event and glucoseexcursion event and establish a causal link between the meal event andglucose excursion event entities in the lifestyle layer. In response todiscovering a relationship between previously unconnected nodes orentities within the graph data structure associated with the respectivelogical database layer, the data management process 200 creates orotherwise generates updated graph metadata characterizing the identifiedrelationship between nodes and stores or otherwise maintains the updatedgraph metadata in the database in association with the logical databaselayer (tasks 206, 208). In this regard, the data management application110 updates the graph metadata 126 associated with the particularlogical database layer to create new directional edges or links betweenpreviously unconnected nodes or entities within that logical databaselayer that were identified as having a causal relationship.

As one example, the data management application 110 may detect a patternwhere meals with more than a threshold amount of fat (e.g., more than 50grams) result in a hyperglycemic excursion event having longer than athreshold duration (e.g., more than 45 minutes), and thereby establish adirectional link or edge between one or more meal event nodes havingmore than the threshold amount of fat and the correspondinghyperglycemic excursion outcome node. The newly created edges may beassigned a weighting or other quantitative value that corresponds to orotherwise reflects the strength of the relationship between the nodes(e.g., based on a probabilistic analysis of the rate of occurrence ofthe outcome). As another example, it may be determined that for aparticular group of patients having certain characteristics in common,exercising more than a threshold number of times per week (e.g., 3 ormore times per week) results in an increase in insulin sensitivity,thereby establish a directional link or edge between certain exerciseevent nodes and an increased insulin sensitivity outcome node with aweighting corresponding to the relative probability of an increase ininsulin sensitivity resulting from the respective exercise event.

Still referring to FIG. 2, in exemplary embodiments, the data managementprocess 200 also analyzes query logs associated with the respectivelogical database layers to identify relationships between previouslyunconnected nodes or entities within that logical database layer basedon the results of previously executed queries. In this regard, thedatabase 104 may store or otherwise maintain a query log where inresponse to executing a query statement, a corresponding log entry iscreated that maintains an association between the logical database layerbeing queried and the query path resulting from execution of the querystatement (e.g., the sequence of nodes and edges traversed within thatlogical database layer during execution of the query statement).

In exemplary embodiments, for each logical database layer, theillustrated data management process 200 periodically analyzes the querylogs associated with that logical database layer to identify logicalrelationships between nodes or entities within that logical databaselayer based on repeated queries that traverse those nodes or entities(tasks 210, 212). For example, in one or more embodiment, the datamanagement application 110 analyzes the query logs associated with aparticular logical database layer to identify or retrieve query pathsthat traverse more than a threshold number of nodes or entities withinthe database layer (e.g., more than 3 nodes). Within that subset ofquery paths traversing more than the threshold number of nodes, the datamanagement application 110 identifies repeated query paths having commonend nodes and establishes logical relationships between those end nodeswithin the logical database layer. In this regard, the data managementprocess 200 creates or otherwise generates updated graph metadataestablishing a logical relationship between the previously unconnectedend nodes and stores or otherwise maintains the updated graph metadatain the database in association with the logical database layer (tasks214, 216). In this manner, the data management application 110 createsnew edges or links between the end nodes of repeated query paths havingcommon end nodes and traversing more than a threshold number of nodes.

By virtue of the data management process 200 creating or otherwiseestablishing relationships between previously unconnected nodes withinthe graph data structure for a particular logical database layer,subsequent queries of that logical database layer may be executed orperformed more efficiently, or otherwise provide improved results thatreflect likely causal and/or logical relationships between nodes of thegraph data structure. In exemplary embodiments, the data managementprocess 200 is performed for each different logical database layer, andthe data management process 200 may repeat periodically (e.g., daily,weekly, monthly, or the like) to continually analyze and update therelationships between the nodes or entities within the respectivelogical database layers.

FIG. 3 depicts an exemplary querying process 300 suitable for querying adatabase having a plurality of different logical layers, such as thedatabase 104 in the patient data management system 100 of FIG. 1. Forpurposes of explanation, the querying process 300 may be describedherein primarily in the context of input queries received from humanusers or patients in a conversational form; however, it should beappreciated that the querying process 300 is not limited toconversational input queries received from users, and the queryingprocess 300 could be implemented in an equivalent manner for queriesthat are neither submitted or initiated by users nor provided in aconversational form. The various tasks performed in connection with thequerying process 300 may be performed by hardware, firmware, softwareexecuted by processing circuitry, or any combination thereof. Forillustrative purposes, the following description refers to elementsmentioned above in connection with FIG. 1. In practice, portions of thequerying process 300 may be performed by different elements of thepatient data management system 100; however, for purposes ofexplanation, the querying process 300 may be described primarily in thecontext of implementation at or by the server 102 and/or the querymanagement application 112. It should be appreciated that the queryingprocess 300 may include any number of additional or alternative tasks,the tasks need not be performed in the illustrated order and/or thetasks may be performed concurrently, and/or the querying process 300 maybe incorporated into a more comprehensive procedure or process havingadditional functionality not described in detail herein. Moreover, oneor more of the tasks shown and described in the context of FIG. 3 couldbe omitted from a practical embodiment of the querying process 300 aslong as the intended overall functionality remains intact.

In the illustrated embodiment, the querying process 300 receives orotherwise obtains an input query from a patient or other user (task302). For example, a patient may interact with or otherwise manipulate auser interface associated with a client application on an electronicdevice 106 to create an input query, which is then transmitted orotherwise provided to the server 102 via the network 108. In one or moreembodiments, the input query is realized as a conversational string ofwords or text provided to the server 102. In this regard, the inputquery may be created or otherwise provided by the user in a free-form orunstructured manner using natural language rather than a predefinedsyntax. For example, in one or more embodiments, the electronic device106 includes an audio input device and a speech recognition engine orvocabulary that supports parsing or otherwise resolving a conversationalspeech or audio input by a user of the device 106 into a correspondingtextual representation to be provided to the server 102. In variousembodiments, the conversational input query may be received unprompted,or alternatively, the user may manipulate the device 106 to select orotherwise activate a graphical user interface (GUI) element that enablesor initiates the querying process 300. For example, in one or moreembodiments, the querying process 300 may be initiated in response to auser selecting a GUI element for a search feature, a digital assistant,or similar feature supported by a client application at the device 106.In response, the client application at the device 106 may generate orotherwise provide a GUI display or other GUI elements that prompts theuser to indicate what he or she would like to know or inquire about.Thereafter, the user may input a conversational string of words (e.g.,via voice, typing, swiping, touch, or any other suitable input method),with a textual representation of the conversational input query beingprovided by the device 106 to the server 102 over the network 108.

In exemplary embodiments, the querying process 300 also receives orotherwise obtains contextual information associated with the input queryfrom the client electronic device providing the input query (task 304).The operational context information provided along with the input querycharacterizes the current operational state or environment at the timeof the input query. For example, in association with a submitted inputquery, the client device 106 may also transmit or otherwise providecontextual information pertaining to the operations of the client device106, such as, for example, the current location of the client device106, the current local time and the current day of week at the locationof the client device 106, the current environmental conditions at thelocation of the client device 106, and/or the like. Additionally, insome embodiments, the client device 106 may also provide informationindicative of the current physiological condition of the user and/or thecurrent operational status of an infusion device or other medical deviceassociated with the user. For example, along with the input query, theclient device 106 may transmit or otherwise provide one or more of acurrent or most recent glucose measurement associated with the user,indication of whether delivery of insulin by an infusion deviceassociated with the user is suspended or not, the current or most recentinsulin delivery to the user, the current or most recent heart ratemeasurement associated with the user, an acceleration measurement orother measurement of an activity level associated with the user, and/orthe like.

The illustrated querying process 300 continues by identifying orotherwise determining which one or more logical database layers shouldbe queried based at least in part on the input query and then generatingor otherwise constructing one or more corresponding query statements tobe executed on the identified logical database layers based at least inpart on the input query (tasks 306, 308). In this regard, the querymanagement application 112 at the server 102 may analyze the input queryto identify or otherwise determine the probable intent or objective ofthe query, and then determine which logical database layers to bequeried based on the intent or objective of the query. In someembodiments, the query management application 112 at the server 102 mayalso analyze the context information associated with the input queryalong with the content of the input query when determining which oflogical database layers should be queried. Once the logical layers to bequeried are identified, the query management application 112 at theserver 102 analyzes the content of the input query to obtain parametersor criteria to be utilized for the querying and then generates orotherwise constructs query statements for execution on the identifiedlogical database layers using those parameters or criteria.Additionally, in some embodiments, the query management application 112at the server 102 may also utilize operational context informationassociated with the input query for one or more parameters or criteriawhen constructing the query statements.

After constructing query statements, the querying process 300 executesor otherwise initiates execution of the constructed query statements onthe identified logical database layer(s) to obtain results for the inputquery from the identified logical database layer(s) (task 310). In thisregard, when the constructed query statements are linked or otherwisedepend on one another, the query management application 112 at theserver 102 may initiate a first query statement on a first logical layerof the database 104 to obtain results to that intermediary querystatement, which, in turn, are utilized by a second query statementperformed on a different logical layer of the database 104. For example,results obtained from querying one logical database layer may beutilized as parameters or criteria in a subsequent query statement on adifferent logical database layer. Additionally, in some embodiments, theresults obtained from querying one logical database layer may befiltered, processed, analyzed, or otherwise optimized to determine theparameters or criteria for use in a subsequent query statement on adifferent logical database layer, as described in greater detail belowin the context of FIG. 4.

To execute a query statement, the database 104 utilizes the graphmetadata 126 to traverse the nodes or entities within the queriedlogical database layer in accordance with the established edges or linksbetween the nodes or entities within that logical database layer toobtain results for the query statement. It should be noted that byvirtue of the weighted directed graph data structures utilized tomaintain data in the database 104, the response time for executing querystatements at the database 104 is typically less than traditionaldatabases reliant on primary key and/or foreign key based table scanningby supporting point-based index referencing that does not requirecomplex table scanning sequences. In exemplary embodiments, a query pathdetailing the nodes or entities and corresponding edges traversed duringexecution of the query statement is also stored or otherwise maintainedin a query log at one of the server 102 or the database 104 inassociation with the queried logical database layer, as described above.

In one or more exemplary embodiments, the querying process 300 filtersthe initial query results based on the operational context informationassociated with the input query prior to generating an output queryresult responsive to the received input query based on the filteredquery results (task 312). In this regard, the initial query results maybe analyzed with respect to the current operational context associatedwith the input query to select or otherwise identify a subset of theinitial query results that is most relevant to one or more aspects ofthe current operational context. The query management application 112 atthe server 102 may select, from among the initial query results obtainedby executing the query statements, a subset of information that is mostlikely to be relevant to the current location of the client device 106,the current local time of day at the location of the client device 106,the current day of the week, the current environmental conditions at thelocation of the client device 106, the current physiological conditionof the patient, the current operational status of an infusion device orother medical device, and/or the like. For example, the query managementapplication 112 may select one of the initial query results that isclosest to or within a threshold distance of the current location of theclient device 106. As another example, the query management application112 may select one of the initial query results that is most likely toyield the best patient outcome based on the patient's current glucoselevel, the current operational status of the patient's infusion device(e.g., delivery suspended, reservoir depletion, or the like).

The querying process 300 generates or otherwise constructs a response tothe input query based on the filtered query results and then presents orotherwise provides the query response in response to the input query(tasks 314, 316). For example, in one or more embodiments, the querymanagement application 112 generates a conversational query responseusing the filtered query results and then transmits the conversationalquery response to the querying client device 106 for presentation orreproduction within the context of a conversation that includes theconversational input query.

FIG. 4 depicts an exemplary embodiment of a graphical user interface(GUI) display 400 that may be presented at a querying client device 406(e.g., one of devices 106) in the patient data management system 100 ofFIG. 1 in connection with the querying process 300 of FIG. 3. The GUIdisplay 400 includes a dialog box or one or more similar GUI elementsthat prompt a user to interact with the client device 106, 406conversationally to query the database 104. In the illustratedembodiment, a patient using the querying client device 106, 406 utilizesan input device or user interface at the client device 106, 406 to inputor otherwise provide a conversational input query. In response, anapplication at the client device 106, 406 updates the GUI display 400 tographically depict a textual representation of the conversational inputquery 402 received by the client device 106, 406. The application at theclient device 106, 406 transmits, submits, or otherwise provides theconversational input query text to the query management application 112at the server 102 for execution.

As described above in the context of FIG. 3, the query managementapplication 112 analyzes the conversational input query text “Whatshould I eat now?” to determine the intent or objective of the inputquery (e.g., intent=find food), the subject of the input query (e.g.,subject=patient identifier), and any other temporal or contextualparameters contained within or associated with the input query (e.g.,time=now). Based on identifying the subject of the input query as thepatient, the query management application 112 may determine that thelifestyle logical layer of the database 104 should be queried to obtainlifestyle information pertaining to the patient. In this regard, thequery management application 112 may construct an initial querystatement for querying the lifestyle logical layer of the database 104using the patient's unique identifier to retrieve lifestyle informationassociated with the patient. For example, executing the query on thelifestyle logical layer of the database 104 may return informationindicating the current or recent type of diet that the patient has beenconsuming (e.g., low carb), information pertaining to the patient'sexercise habits or other recent activity by the patient, and/orpotentially other contextual information characterizing the patient'slifestyle. Additionally, based on identifying the subject of the inputquery as the patient, the query management application 112 may alsoquery the patient logical layer of the database 104 to identify otherpatient's similar to the patient that is the subject of the input query(e.g., based on the edges or links in the graph metadata 126 for thepatient logical layer linking those other patients with the currentpatient via more than a threshold number of common nodes or entities).

Using the lifestyle information obtained from querying the lifestylelogical layer and the identifiers for other patient's similar to thesubject patient, the query management application 112 generates orotherwise constructs a query statement for querying the patient logicallayer to obtain meal logs or other meal information associated with asubset of the similar patients that have similar lifestyle informationassociated therewith (e.g., patients that have similar exercise oractivity behavior, geographic location, and/or the like) and for whichthe outcome of the meals were good (e.g., no hypoglycemic orhyperglycemic events or other excursion events following the meals forhaving similar lifestyle contexts, postprandial glucose within athreshold amount of a patient's target glucose value, etc.). Afterobtaining information for the meals consumed by similar patients thathad positive outcomes from the patient logical layer, the querymanagement application 112 may generates or otherwise constructs a querystatement for querying the lifestyle logical layer to identify a subsetof those meals that best match or are most closely associated with thepatient's lifestyle information (e.g., meals associated with low carbdiets or other patient's having low carb diets associated therewith,and/or the like). In one or more exemplary embodiments, the querystatement may also account for the current operational context for thepatient (e.g., meals within a threshold distance of the current locationof the client device 106, 406, and/or the like). In yet otherembodiments, the current operational context is utilized to filter orotherwise exclude query results and identify a meal result that bestmatches the querying patient's current operational context andlifestyle.

After obtaining a meal result from the querying the lifestyle logicallayer that best matches the patient's lifestyle and current operationalcontext and achieved a positive outcome for one or more similarpatients, the query management application 112 generates or otherwiseconstructs a query response that includes or incorporates that mealresult in a conversational form. In this regard, in one or moreembodiments, based on the identified meal type and the current locationassociated with the input query, the query management application 112queries a database of restaurant information including geographiclocation information and menu data associated with a plurality ofrestaurants to identify a restaurant closest to or otherwise in thevicinity of the current location of the querying device 106, 406 thatserves an item that matches or corresponds to the identified meal. Thequery management application 112 may then generate query response textthat indicates the identified restaurant and menu item that best matchesthe meal result from querying the database 104. The query managementapplication 112 transmits or otherwise provides the conversational queryresponse text to the querying client device 106, 406 for presentation atthe client device 106, 406. An application at the client device 106, 406updates the GUI display 400 to graphically depict the textualrepresentation of the conversational query response 404 received by theclient device 106, 406 from the server 102 within the context of theconversation including the conversational input query 402.

FIG. 5 depicts an exemplary graphical representation of a partial graphdata structure 500 corresponding to a subset of a patient logical layerin the database 104 that depicts the relationships between differentpatients that may be related to a query subject patient based on thegraph metadata 126. In this regard, FIG. 5 depicts a node 502 within thepatient logical layer that is associated with a querying patient. Basedon the graph metadata 126, the querying patient node 502 is associatedwith a plurality of different entity nodes 504 within the patientlogical layer, with those entity nodes 504 corresponding to differentfields or subsets of the data 120, 122, 124 in the database 104 that areassociated with the querying patient and mapped to the various nodesbased on the graph metadata 126 for the patient logical layer.Additionally, the graph metadata 126 for the patient logical layer mayalso define edges or links between the entity nodes 504 associated withthe querying patient 502 to one or more other patient nodes 506associated with different patients having similar values for theirassociated fields or subsets of the data 120, 122, 124 in the database104 that map to those entity nodes 504. In some embodiments, the edgesbetween an entity node 504 and a similar patient node 506 may beassigned a weight based on the difference or similarity between thevalue(s) of the querying patient's associated fields or subsets of thedata 120, 122, 124 that map to that node 504 and the value(s) of thosefields or subsets of the data 120, 122, 124 associated with therespective patient having his or her patient node 506 linked to therespective entity node 504. Thus, both the number of edges betweenrespective pairs of related patient nodes 502, 506 and the respectiveweightings assigned to those edges may be utilized to calculate orotherwise determine a metric indicative of the relative similaritybetween the query subject associated with patient node 502 and adifferent patient associated with one of patient nodes 506.

As described above in the context of FIG. 4, the graph metadata 126associated with the patient logical layer in the database 104 may beutilized when executing a query statement on the patient logical layerto obtain patient identifiers associated with patient nodes 506 that aremost similar to the query subject (e.g., based on a similarity metriccharacterizing the relationship between respective pairs of patientnodes 502, 506). The patient identifiers associated with the similarpatient nodes 506 may then be utilized to query other logical layers ofthe database 104 to obtain information indicative of meals, activities,medications, therapies, and/or the like by those patients and how suchvariables affected those patients' physiological conditions (e.g.,glucose measurements, excursion events, and/or the like) to generaterecommendations or otherwise provide query results that are most likelyto achieve the best outcome in regards to the physiological condition ofthe query subject patient.

In some embodiments, the nodes 504 could reside in a different logicallayer of the database 104 than the patient nodes 502, 506, withrespective pairs of patient nodes 502, 506 having at least a thresholdnumber of nodes 504 in common or shared within another logical layerbeing utilized to establish a relationship between the respective pairof patient nodes 502, 506 or otherwise classify the pair of patientnodes 502, 506 to a common group or cohort, as described in greaterdetail below.

Cognitive Pump

Referring now to FIG. 6, in accordance with one or more exemplaryembodiments, an infusion device 602 in an infusion system 600 isutilized as an electronic device 106 capable of querying the database104 via the server 102 in the patient data management system 100 ofFIG. 1. In this regard, the infusion device 602 is capable of receivinga conversational user input as well as capturing contemporaneous,concurrent, or otherwise temporally relevant operational contextinformation associated with conversational user input and providingcorresponding conversational input query text and associated contextinformation to the query management application 112 at the server 102 inaccordance with the querying process 300 of FIG. 3.

In exemplary embodiments, the infusion system 600 is also capable ofcontrolling or otherwise regulating a physiological condition in thebody 601 of a user to a desired (or target) value or otherwise maintainthe condition within a range of acceptable values in an automated orautonomous manner. In one or more exemplary embodiments, the conditionbeing regulated is sensed, detected, measured or otherwise quantified bya sensing arrangement 604 (e.g., sensing arrangement 604)communicatively coupled to the infusion device 602. However, it shouldbe noted that in alternative embodiments, the condition being regulatedby the infusion system 600 may be correlative to the measured valuesobtained by the sensing arrangement 604. That said, for clarity andpurposes of explanation, the subject matter may be described herein inthe context of the sensing arrangement 604 being realized as a glucosesensing arrangement that senses, detects, measures or otherwisequantifies the user's glucose level, which is being regulated in thebody 601 of the user by the infusion system 600.

In exemplary embodiments, the sensing arrangement 604 includes one ormore interstitial glucose sensing elements that generate or otherwiseoutput electrical signals (alternatively referred to herein asmeasurement signals) having a signal characteristic that is correlativeto, influenced by, or otherwise indicative of the relative interstitialfluid glucose level in the body 601 of the user. The output electricalsignals are filtered or otherwise processed to obtain a measurementvalue indicative of the user's interstitial fluid glucose level. Inexemplary embodiments, a blood glucose meter 630, such as a finger stickdevice, is utilized to directly sense, detect, measure or otherwisequantify the blood glucose in the body 601 of the user. In this regard,the blood glucose meter 630 outputs or otherwise provides a measuredblood glucose value that may be utilized as a reference measurement forcalibrating the sensing arrangement 604 and converting a measurementvalue indicative of the user's interstitial fluid glucose level into acorresponding calibrated blood glucose value. For purposes ofexplanation, the calibrated blood glucose value calculated based on theelectrical signals output by the sensing element(s) of the sensingarrangement 604 may alternatively be referred to herein as the sensorglucose value, the sensed glucose value, or variants thereof.

In exemplary embodiments, the infusion system 600 also includes one ormore additional sensing arrangements 606, 608 configured to sense,detect, measure or otherwise quantify a characteristic of the body 601of the user that is indicative of a condition in the body 601 of theuser. In this regard, in addition to the glucose sensing arrangement604, one or more auxiliary sensing arrangements 606 may be worn,carried, or otherwise associated with the body 601 of the user tomeasure characteristics or conditions of the user (or the user'sactivity) that may influence the user's glucose levels or insulinsensitivity. For example, a heart rate sensing arrangement 606 could beworn on or otherwise associated with the user's body 601 to sense,detect, measure or otherwise quantify the user's heart rate, which, inturn, may be indicative of exercise (and the intensity thereof) that islikely to influence the user's glucose levels or insulin response in thebody 601. In yet another embodiment, another invasive, interstitial, orsubcutaneous sensing arrangement 606 may be inserted into the body 601of the user to obtain measurements of another physiological conditionthat may be indicative of exercise (and the intensity thereof), such as,for example, a lactate sensor, a ketone sensor, or the like. Dependingon the embodiment, the auxiliary sensing arrangement(s) 606 could berealized as a standalone component worn by the user, or alternatively,the auxiliary sensing arrangement(s) 606 may be integrated with theinfusion device 602 or the glucose sensing arrangement 604.

The illustrated infusion system 600 also includes an accelerationsensing arrangement 608 (or accelerometer) that may be worn on orotherwise associated with the user's body 601 to sense, detect, measureor otherwise quantify an acceleration of the user's body 601, which, inturn, may be indicative of exercise or some other condition in the body601 that is likely to influence the user's insulin response. While theacceleration sensing arrangement 608 is depicted as being integratedinto the infusion device 602 in FIG. 6, in alternative embodiments, theacceleration sensing arrangement 608 may be integrated with anothersensing arrangement 604, 606 on the body 601 of the user, or theacceleration sensing arrangement 608 may be realized as a separatestandalone component that is worn by the user.

In exemplary embodiments, the infusion device 602 also includes one ormore environmental sensing arrangements 650 to sense, detect, measure orotherwise quantify the current operating environment around the infusiondevice 602. In this regard, the environmental sensing arrangements 650may include one or more of a temperature sensing arrangement (orthermometer), a humidity sensing arrangement, a pressure sensingarrangement (or barometer), and/or the like. In exemplary embodiments,the infusion device 602 also includes a position sensing arrangement 660to sense, detect, measure or otherwise quantify the current geographiclocation of the infusion device 602, such as, for example, a globalpositioning system (GPS) receiver.

In the illustrated embodiment, the pump control system 620 generallyrepresents the electronics and other components of the infusion device602 that control operation of the fluid infusion device 602 according toa desired infusion delivery program in a manner that is influenced bythe sensed glucose value indicating the current glucose level in thebody 601 of the user. For example, to support a closed-loop operatingmode, the pump control system 620 maintains, receives, or otherwiseobtains a target or commanded glucose value, and automatically generatesor otherwise determines dosage commands for operating an actuationarrangement, such as a motor 632, to displace the plunger 617 anddeliver insulin to the body 601 of the user based on the differencebetween the sensed glucose value and the target glucose value. In otheroperating modes, the pump control system 620 may generate or otherwisedetermine dosage commands configured to maintain the sensed glucosevalue below an upper glucose limit, above a lower glucose limit, orotherwise within a desired range of glucose values. In practice, theinfusion device 602 may store or otherwise maintain the target value,upper and/or lower glucose limit(s), insulin delivery limit(s), and/orother glucose threshold value(s) in a data storage element accessible tothe pump control system 620.

Still referring to FIG. 6, the target glucose value and other thresholdglucose values utilized by the pump control system 620 may be receivedfrom an external component or be input by a user via a user interfaceelement 640 associated with the infusion device 602. In practice, theone or more user interface element(s) 640 associated with the infusiondevice 602 typically include at least one input user interface element,such as, for example, a button, a keypad, a keyboard, a knob, ajoystick, a mouse, a touch panel, a touchscreen, a microphone or anotheraudio input device, and/or the like. Additionally, the one or more userinterface element(s) 640 include at least one output user interfaceelement, such as, for example, a display element (e.g., a light-emittingdiode or the like), a display device (e.g., a liquid crystal display orthe like), a speaker or another audio output device, a haptic feedbackdevice, or the like, for providing notifications or other information tothe user. It should be noted that although FIG. 6 depicts the userinterface element(s) 640 as being separate from the infusion device 602,in practice, one or more of the user interface element(s) 640 may beintegrated with the infusion device 602. Furthermore, in someembodiments, one or more user interface element(s) 640 are integratedwith the sensing arrangement 604 in addition to and/or in alternative tothe user interface element(s) 640 integrated with the infusion device602. The user interface element(s) 640 may be manipulated by the user tooperate the infusion device 602 to deliver correction boluses, adjusttarget and/or threshold values, modify the delivery control scheme oroperating mode, and the like, as desired.

Still referring to FIG. 6, in the illustrated embodiment, the infusiondevice 602 includes a motor control module 612 coupled to a motor 632that is operable to displace a plunger 617 in a reservoir and provide adesired amount of fluid to the body 601 of a user. In this regard,displacement of the plunger 617 results in the delivery of a fluid, suchas insulin, that is capable of influencing the user's physiologicalcondition to the body 601 of the user via a fluid delivery path (e.g.,via tubing of an infusion set). A motor driver module 614 is coupledbetween an energy source 618 and the motor 632. The motor control module612 is coupled to the motor driver module 614, and the motor controlmodule 612 generates or otherwise provides command signals that operatethe motor driver module 614 to provide current (or power) from theenergy source 618 to the motor 632 to displace the plunger 617 inresponse to receiving, from a pump control system 620, a dosage commandindicative of the desired amount of fluid to be delivered.

In exemplary embodiments, the energy source 618 is realized as a batteryhoused within the infusion device 602 that provides direct current (DC)power. In this regard, the motor driver module 614 generally representsthe combination of circuitry, hardware and/or other electricalcomponents configured to convert or otherwise transfer DC power providedby the energy source 618 into alternating electrical signals applied torespective phases of the stator windings of the motor 632 that result incurrent flowing through the stator windings that generates a statormagnetic field and causes the rotor of the motor 632 to rotate. Themotor control module 612 is configured to receive or otherwise obtain acommanded dosage from the pump control system 620, convert the commandeddosage to a commanded translational displacement of the plunger 617, andcommand, signal, or otherwise operate the motor driver module 614 tocause the rotor of the motor 632 to rotate by an amount that producesthe commanded translational displacement of the plunger 617. Forexample, the motor control module 612 may determine an amount ofrotation of the rotor required to produce translational displacement ofthe plunger 617 that achieves the commanded dosage received from thepump control system 620. Based on the current rotational position (ororientation) of the rotor with respect to the stator that is indicatedby the output of the rotor sensing arrangement 616, the motor controlmodule 612 determines the appropriate sequence of alternating electricalsignals to be applied to the respective phases of the stator windingsthat should rotate the rotor by the determined amount of rotation fromits current position (or orientation). In embodiments where the motor632 is realized as a BLDC motor, the alternating electrical signalscommutate the respective phases of the stator windings at theappropriate orientation of the rotor magnetic poles with respect to thestator and in the appropriate order to provide a rotating statormagnetic field that rotates the rotor in the desired direction.Thereafter, the motor control module 612 operates the motor drivermodule 614 to apply the determined alternating electrical signals (e.g.,the command signals) to the stator windings of the motor 632 to achievethe desired delivery of fluid to the user.

When the motor control module 612 is operating the motor driver module614, current flows from the energy source 618 through the statorwindings of the motor 632 to produce a stator magnetic field thatinteracts with the rotor magnetic field. In some embodiments, after themotor control module 612 operates the motor driver module 614 and/ormotor 632 to achieve the commanded dosage, the motor control module 612ceases operating the motor driver module 614 and/or motor 632 until asubsequent dosage command is received. In this regard, the motor drivermodule 614 and the motor 632 enter an idle state during which the motordriver module 614 effectively disconnects or isolates the statorwindings of the motor 632 from the energy source 618. In other words,current does not flow from the energy source 618 through the statorwindings of the motor 632 when the motor 632 is idle, and thus, themotor 632 does not consume power from the energy source 618 in the idlestate, thereby improving efficiency.

Depending on the embodiment, the motor control module 612 may beimplemented or realized with a general purpose processor, amicroprocessor, a controller, a microcontroller, a state machine, acontent addressable memory, an application specific integrated circuit,a field programmable gate array, any suitable programmable logic device,discrete gate or transistor logic, discrete hardware components, or anycombination thereof, designed to perform the functions described herein.In exemplary embodiments, the motor control module 612 includes orotherwise accesses a data storage element or memory, including any sortof random access memory (RAM), read only memory (ROM), flash memory,registers, hard disks, removable disks, magnetic or optical massstorage, or any other short or long term storage media or othernon-transitory computer-readable medium, which is capable of storingprogramming instructions for execution by the motor control module 612.The computer-executable programming instructions, when read and executedby the motor control module 612, cause the motor control module 612 toperform or otherwise support the tasks, operations, functions, andprocesses described herein.

It should be appreciated that FIG. 6 is a simplified representation ofthe infusion device 602 for purposes of explanation and is not intendedto limit the subject matter described herein in any way. In this regard,depending on the embodiment, some features and/or functionality of thesensing arrangement 604 may implemented by or otherwise integrated intothe pump control system 620, or vice versa. Similarly, in practice, thefeatures and/or functionality of the motor control module 612 mayimplemented by or otherwise integrated into the pump control system 620,or vice versa. Furthermore, the features and/or functionality of thepump control system 620 may be implemented by control electronicslocated in the fluid infusion device 602, while in alternativeembodiments, the pump control system 620 may be implemented by a remotecomputing device that is physically distinct and/or separate from theinfusion device 602 (e.g., a mobile computing device communicativelycoupled to the infusion device 602 over a personal area network or thelike).

FIG. 7 depicts an exemplary embodiment of a pump control system 700suitable for use as the pump control system 620 in FIG. 6 in accordancewith one or more embodiments. The illustrated pump control system 700includes, without limitation, a pump control module 702, acommunications interface 704, and a data storage element (or memory)706. The pump control module 702 is coupled to the communicationsinterface 704 and the memory 706, and the pump control module 702 issuitably configured to support the operations, tasks, and/or processesdescribed herein. In various embodiments, the pump control module 702 isalso coupled to one or more user interface elements (e.g., userinterface 640) for receiving user inputs (e.g., target glucose values orother glucose thresholds) and providing notifications, alerts, or othertherapy information to the user.

The communications interface 704 generally represents the hardware,circuitry, logic, firmware and/or other components of the pump controlsystem 700 that are coupled to the pump control module 702 andconfigured to support communications between the pump control system 700and one or more of the various sensing arrangements 604, 606, 608, 650,660. In this regard, the communications interface 704 may include orotherwise be coupled to one or more transceiver modules capable ofsupporting wireless communications between the pump control system 620,700 and an external sensing arrangement 604, 606. For example, thecommunications interface 704 may be utilized to wirelessly receivesensor measurement values or other measurement data from each externalsensing arrangement 604, 606 in an infusion system 600. In otherembodiments, the communications interface 704 may be configured tosupport wired communications to/from the external sensing arrangement(s)604, 606. In various embodiments, the communications interface 704 mayalso support communications with a remote server (e.g., server 102) oranother electronic device in an infusion system (e.g., to upload sensormeasurement values, receive control information, and the like).

The pump control module 702 generally represents the hardware,circuitry, logic, firmware and/or other component of the pump controlsystem 700 that is coupled to the communications interface 704 and thesensing arrangements 604, 606, 608, 650, 660 and configured to determinedosage commands for operating the motor 632 to deliver fluid to the body601 based on measurement data received from the sensing arrangements604, 606, 608, 650, 660 and perform various additional tasks,operations, functions and/or operations described herein. For example,in exemplary embodiments, pump control module 702 implements orotherwise executes a command generation application 710 that supportsone or more autonomous operating modes and calculates or otherwisedetermines dosage commands for operating the motor 632 of the infusiondevice 602 in an autonomous operating mode based at least in part on acurrent measurement value for a condition in the body 601 of the user.For example, in a closed-loop operating mode, the command generationapplication 710 may determine a dosage command for operating the motor632 to deliver insulin to the body 601 of the user based at least inpart on the current glucose measurement value most recently receivedfrom the sensing arrangement 604 to regulate the user's blood glucoselevel to a target reference glucose value. In various embodiments, thedosage commands may also be adjusted or otherwise influenced bycontextual measurement data, that is, measurement data thatcharacterizes, quantifies, or otherwise indicates the contemporaneous orconcurrent operating context for the dosage command(s), such as, forexample, environmental measurement data obtained from an environmentalsensing arrangement 650, the current location information obtained froma GPS receiver 660 and/or other contextual information characterizingthe current operating environment for the infusion device 602.Additionally, the command generation application 710 may generate dosagecommands for boluses that are manually-initiated or otherwise instructedby a user via a user interface element.

In one or more exemplary embodiments, the pump control module 702 alsoimplements or otherwise executes a prediction application 708 (orprediction engine) that is configured to estimate or otherwise predictthe future physiological condition and potentially other futureactivities, events, operating contexts, and/or the like in apersonalized, patient-specific (or patient-specific) manner. In thisregard, in some embodiments, the prediction engine 708 cooperativelyconfigured to interact with the command generation application 710 tosupport adjusting dosage commands or control information dictating themanner in which dosage commands are generated in a predictive orprospective manner. In this regard, in some embodiments, based oncorrelations between current or recent measurement data and the currentoperational context relative to historical data associated with thepatient, the prediction engine 708 may forecast or otherwise predictfuture glucose levels of the patient at different times in the future,and correspondingly adjust or otherwise modify values for one or moreparameters utilized by the command generation application 710 whendetermining dosage commands in a manner that accounts for the predictedglucose level, for example, by modifying a parameter value at a registeror location in memory 706 referenced by the command generationapplication 710. In various embodiments, the prediction engine 708 maypredict meals or other events or activities that are likely to beengaged in by the patient and output or otherwise provide an indicationof how the patient's predicted glucose level is likely to be influencedby the predicted events, which, in turn, may then be reviewed orconsidered by the patient to prospectively adjust his or her behaviorand/or utilized to adjust the manner in which dosage commands aregenerated to regulate glucose in a manner that accounts for thepatient's behavior in a personalized manner.

In one or more exemplary embodiments, the pump control module 702 alsoimplements or otherwise executes a conversational interactionapplication 712 that is configured to support conversationalinteractions with a patient or other user. For example, theconversational interaction application 712 may generate or otherwiseprovide a GUI display on a display device 640 associated with aninfusion device 602 that includes a dialog box that prompts a user toconversationally interact with the infusion device 602. In this regard,in one or more embodiments, the conversational interaction application712 may generate a GUI display that prompts a user to conversationallyquery or search a database system 104, such as GUI display 400. Theconversational interaction application 712 may also supportconversationally monitoring or managing a patient's physiologicalcondition, as described above in the context of FIGS. 3-4 and in greaterdetail below in the context of FIGS. 13-16. In one or more exemplaryembodiments, the pump control module 702 also implements or otherwiseexecutes a recommendation application 714 (or recommendation engine)that is configured to support providing therapy recommendations to thepatient, as described in greater detail below in the context of FIGS.17-19.

Still referring to FIG. 7, depending on the embodiment, the pump controlmodule 702 may be implemented or realized with a general purposeprocessor, a microprocessor, a controller, a microcontroller, a statemachine, a content addressable memory, an application specificintegrated circuit, a field programmable gate array, any suitableprogrammable logic device, discrete gate or transistor logic, discretehardware components, or any combination thereof, designed to perform thefunctions described herein. In this regard, the steps of a method oralgorithm described in connection with the embodiments disclosed hereinmay be embodied directly in hardware, in firmware, in a software moduleexecuted by the pump control module 702, or in any practical combinationthereof. In exemplary embodiments, the pump control module 702 includesor otherwise accesses the data storage element or memory 706, which maybe realized using any sort of non-transitory computer-readable mediumcapable of storing programming instructions for execution by the pumpcontrol module 702. The computer-executable programming instructions,when read and executed by the pump control module 702, cause the pumpcontrol module 702 to implement or otherwise generate the applications708, 710, 712, 714 and perform tasks, operations, functions, andprocesses described herein.

It should be understood that FIG. 7 is a simplified representation of apump control system 700 for purposes of explanation and is not intendedto limit the subject matter described herein in any way. For example, insome embodiments, the features and/or functionality of the motor controlmodule 612 may be implemented by or otherwise integrated into the pumpcontrol system 700 and/or the pump control module 702, for example, bythe command generation application 710 converting the dosage commandinto a corresponding motor command, in which case, the separate motorcontrol module 612 may be absent from an embodiment of the infusiondevice 602.

Glucose Predictions and Forecasting

In one or more exemplary embodiments, a patient-specific forecastingmodel for a physiological condition is determined based on historicaldata associated with a patient and utilized to predict future values orlevels of the physiological condition based at least in part on thecurrent operational context and current measurements for thephysiological condition. Additionally, historical event data andassociated context information may be utilized to predict one or morefuture events at different times in the future within the forecasthorizon based at least in part on the current measurement data, and/orthe current operational context (e.g., the current time of day, thecurrent day of the week, the current geographic location, and the like),which, in turn may be input to the patient-specific forecasting model toadjust the forecasted values or levels of the physiological condition atappropriate times in the future to reflect the predicted events. Whilethe subject matter is described herein in the context of glucoseforecasting and predictions, the subject matter is not necessarilylimited to glucose levels and could be implemented in an equivalentmanner to forecast or predict other physiological conditions of anindividual.

In exemplary embodiments, a patient-specific glucose forecasting modelis determined that allows for the patient's glucose level to beforecasted for discrete time intervals in the future. For purposes ofexplanation, the subject matter is described herein in the context ofhourly forecasting that allows for the patient's glucose level to beforecast on an hourly basis; however, it should be noted that thesubject matter described herein is not limited to hourly forecasting andcould be utilized for different forecast time intervals (e.g., on anevery 15-minute basis, a 30-minute basis, every 4 hours, and/or thelike).

FIG. 8 depicts an exemplary forecasting process 800 suitable forimplementation by a computing device, such as a server 102 or clientelectronic device 106 in the patient data management system 100 ofFIG. 1. The various tasks performed in connection with the forecastingprocess 800 may be performed by hardware, firmware, software executed byprocessing circuitry, or any combination thereof. For illustrativepurposes, the following description refers to elements mentioned abovein connection with FIGS. 1 and 6-7. In practice, portions of theforecasting process 800 may be performed by different elements of apatient data management system 100 or an infusion system 600, such as,for example, the server 102, the electronic device(s) 106, an infusiondevice 602, and/or the pump control system 620, 700. It should beappreciated that the forecasting process 800 may include any number ofadditional or alternative tasks, the tasks need not be performed in theillustrated order and/or the tasks may be performed concurrently, and/orthe forecasting process 800 may be incorporated into a morecomprehensive procedure or process having additional functionality notdescribed in detail herein. Moreover, one or more of the tasks shown anddescribed in the context of FIG. 8 could be omitted from a practicalembodiment of the forecasting process 800 as long as the intendedoverall functionality remains intact.

The illustrated embodiment of the forecasting process 800 initializes orotherwise begins by retrieving or otherwise obtaining historical dataassociated with the patient of interest to be modeled and developing,training, or otherwise determining a forecasting model for the patientusing the historical data associated with the patient (tasks 802, 804).In one or more exemplary embodiments, for an individual patient withinthe patient data management system 100, the server 102 periodicallyretrieves or otherwise obtains the historical patient data 120associated with that patient from the database 104 and analyzes therelationships between different subsets of the historical patient data120 to create a patient-specific forecasting model associated with thatpatient. Depending on the embodiment, the patient-specific forecastingmodel may be stored on the database 104 in association with the patientand utilized by the server 102 to determine a glucose forecast for thepatient (e.g., in response to a request from a client device 106) andprovide the resulting glucose forecast to a client device 106 forpresentation to a user. In other embodiments, the server 102 pushes,provides, or otherwise transmits the patient-specific forecasting modelto one or more electronic devices 106 associated with the patient (e.g.,infusion device 602) for implementing and supporting glucose forecastsat the end user device (e.g., by prediction engine 708).

In one or more exemplary embodiments, a recurrent neural network isutilized to create hourly neural network cells that are trained topredict an average glucose level for the patient associated with thatrespective hourly interval based on subsets of historical patient datacorresponding to that hourly interval across a plurality of differentdays preceding development of the model. For example, in one embodiment,for each hourly interval within a day, a corresponding long short-termmemory (LSTM) unit (or cell) is created, with the LSTM unit outputtingan average glucose value for that hourly interval as a function of thesubset of historical patient data corresponding to that hourly intervaland the variables from one or more of the LSTM units preceding thecurrent LSTM unit. For example, a LSTM unit associated with the 1-2 PMtime interval is configured to calculate an average glucose value forthe patient over the 1-2 PM timeframe based on the subset of historicalpatient data timestamped within or otherwise associated with the 1-2 PMtimeframe and the inputs to and/or outputs from one or more precedingLSTM units (e.g., the average glucose value for the patient over the12-1 PM timeframe output by the 12-1 PM LSTM unit, a correlative portionof the subset of historical patient data timestamped within or otherwiseassociated with the 12-1 PM timeframe, and/or the like).

For each LSTM unit, machine learning may be utilized to determine acorresponding equation, function, or model for calculating the averageglucose value for the patient for that time interval based at least inpart on the historical insulin delivery data, historical meal data, andhistorical exercise data for the patient during that time interval. Inthis regard, the model for a particular hourly interval is capable ofcharacterizing or mapping the insulin delivery data during the hourlyinterval, the meal data during the hourly interval, the exercise dataduring the hourly interval, and the average glucose value for thepreceding hourly interval to the average sensor glucose value for thehourly interval being modeled. Additionally, the hourly model mayaccount for historical auxiliary measurement data (e.g., historicalacceleration measurement data, historical heart rate measurement data,and/or the like), historical medication data or other historical eventlog data, historical geolocation data, historical environmental data,and/or other historical or contextual data may be correlative to orpredictive of the average glucose level for the patient during that timeinterval. Thus, as different variables have a greater or lesser impacton the patient's glucose level during the course of the day, theindividual functions or equations associated with the respective LSTMunits may increase or decrease the weighting or emphasis a particularinput variable has on the average glucose value calculated by arespective LSTM unit as appropriate. It should be noted that any numberof different machine learning techniques may be utilized to determinewhat input variables are predictive for a current patient of interestand a current hourly interval of the day, such as, for example,artificial neural networks, genetic programming, support vectormachines, Bayesian networks, probabilistic machine learning models, orother Bayesian techniques, fuzzy logic, heuristically derivedcombinations, or the like.

The forecasting process 800 continues by receiving, retrieving, orotherwise obtaining recent patient data, identifying or otherwiseobtaining the current operational context associated with the patient,and predicting future behavior of the patient based on the recentpatient data and the current operational context (tasks 806, 808, 810).In this regard, predictive models for future insulin deliveries, futuremeals, future exercise events, and/or future medication dosages may bedetermined that characterize or map a particular combination of one ormore of the current (or recent) sensor glucose measurement data,auxiliary measurement data, delivery data, geographic location, mealdata, exercise data, patient behavior or activities, and the like to avalue representative of the current probability or likelihood of aparticular event or activity and/or a current value associated with thatevent or activity (e.g., a predicted meal size, a predicted exerciseduration and/or intensity, a predicted bolus amount, and/or the like).Thus, the forecasting process 800 may obtain from one or more of thesensing arrangements 604, 606, 608 the infusion device 602 and/or thedatabase 104 the current or most recent sensor glucose measurementvalues associated with the patient, along with data or informationquantifying or characterizing recent insulin deliveries, meals,exercise, and potentially other events, activities or behaviors by theuser within a preceding interval of time (e.g., within the preceding 2hours). The forecasting process 800 may also obtain from one or more ofthe sensing arrangements 650, 660, the infusion device 602 and/or thedatabase 104 data or information quantifying or characterizing thecurrent or recent operational contexts associated with the infusiondevice 602.

Based on the current and recent patient measurement data, insulindelivery data, meal data, and exercise data, along with the current timeof day, the current day of the week, and/or other current or recentcontext data, the forecasting process 800 determines event probabilitiesand/or characteristics for future hourly time intervals. For example,for each hourly time interval in the future, the forecasting process 800may determine a meal probability and/or a predicted meal size duringthat future hourly time interval that may be utilized as an input to theLSTM unit for that hourly time interval. Similarly, the forecastingprocess 800 may determine a predicted insulin delivery amount, apredicted exercise probability and/or a predicted exercise intensity orduration, a predicted medication dosage, and/or the like during eachrespective future hourly time interval based on the relationshipsbetween the recent patient data and context data and historical patientdata and context data preceding occurrence of previous instances ofthose events. Some examples of predicting patient behaviors oractivities are described in U.S. patent application Ser. No. 15/847,750,which is incorporated by reference herein.

Still referring to FIG. 8, after predicting future patient behaviorlikely to influence the patient's future glucose levels, the forecastingprocess 800 continues by calculating or otherwise determining forecastedglucose levels for hourly intervals in the future based at least in parton the current or recent glucose measurement data and the predictedfuture behavior and generating or otherwise providing graphicalrepresentations of the forecasted glucose levels associated with thedifferent future hourly intervals (tasks 812, 814). Based on the currenttime of day, the forecasting model for the next hourly interval of theday may be selected and utilized to calculate a forecasted glucose levelfor that hourly interval based at least in part on the recent sensorglucose measurement value(s) and the predicted meals, exercise, insulindeliveries and/or medication dosages for the next hourly interval of theday. For example, the current sensor glucose measurement value andpreceding sensor glucose measurement values obtained within the currenthourly interval may be averaged or otherwise combined to obtain anaverage sensor glucose measurement value for the current hourly intervalthat may be input to the forecasting model for the next hourly intervalof the day. The forecasting model is then utilized to calculate aforecasted average glucose value for the next hourly interval of the daybased on that average sensor glucose measurement value for the currenthourly interval and the predicted patient behavior during the nexthourly interval. The forecasted average glucose value for the nexthourly interval may then be input to the forecasting model for thesubsequent hourly interval for calculating a forecasted glucose valuefor that subsequent hourly interval based on its associated predictedpatient behavior, and so on.

FIG. 9 depicts an exemplary GUI display 900 including a glucose forecastregion 902 that includes graphical representations of forecasted glucoselevels for a patient in association with subsequent hourly intervals ofthe day. In the illustrated GUI display 900, a graphical representation904 of the patient's recent sensor glucose measurement data is presentedadjacent to the glucose forecast region 902. In exemplary embodiments,the sensor glucose measurement display region 904 includes a line chartor line graph of the patient's historical sensor glucose measurementdata with a visually distinguishable overlay region that indicates atarget range for the patient's sensor glucose measurement values.Depending on the embodiment, the GUI display 900 may be presented on adisplay device 640 associated with an infusion device 602 or on anotherelectronic device 102, 106 within a patient data management system 100.In one or more embodiments, the forecasting process 800 is performed togenerate the forecast region 902 on the GUI display 900 in response to apatient selecting a GUI element configured to cause presentation of theforecast region 902 or otherwise requesting presentation of a glucoseforecast (e.g., by conversationally requesting a glucose forecast viaconversational interaction application 712).

Referring to FIGS. 8-9, in one or more exemplary embodiments, based onthe current time of day (e.g., 9:45 AM), the current sensor glucosemeasurement value (e.g., 110 mg/dL), and potentially other recentpatient data (e.g., recent meals, exercise, or boluses) and/or thecurrent operating context, the forecasting process 800 calculates orotherwise determines predicted patient behavior for the 10 AM hourlyinterval and subsequent hourly intervals for which the patient's glucoselevels are to be forecast. FIG. 10 depicts a graphical representation ofa part of a recurrent neural network including hourly LSTM cellsconfigured to calculate forecasted glucose levels depicted in FIG. 9using the predicted patient behavior for future hourly intervals and thepatient's current sensor glucose measurement data. In this regard, anaverage sensor glucose value for the current interval 1001 is input tothe 10 AM hourly interval LSTM cell 1002 along with the predictedpatient behavior 1003 for the 10 AM hourly interval (e.g., the predictedamount of carbohydrates consumed, insulin delivered, exercise,medication, and the like for the patient during the 10 AM to 11 AM timeperiod). Depending on the embodiment, the current interval sensorglucose value 1001 may be realized as the current or most recent sensorglucose measurement value (e.g., 110 mg/dL), an average of the currentand preceding sensor glucose measurement values obtained during thecurrent interval (e.g., an average of the sensor glucose measurementvalues timestamped between 9:00 AM and 9:45 AM), or another sensorglucose value calculated based at least in part on the current sensorglucose measurement value. For example, the current sensor glucosemeasurement value and other recent behavior may be utilized to predictthe patient's glucose level for the remainder of the current timeinterval (e.g., from 9:45 AM to 10 AM), which in turn, may be averaged,weighted, or otherwise combined with the average of the preceding sensorglucose measurement values obtained during the current interval (e.g.,from 9 AM to 9:45 AM) to obtain an estimated average sensor glucosemeasurement value for the current time interval.

Based on the average sensor glucose value for the current interval 1001and the predicted patient behavior 1003 for the 10 AM interval, the LSTMcell 1002 calculates or otherwise determines an average glucose value1005 associated with the 10 AM interval utilizing the forecasting modelfor the 10 AM hourly interval that was determined based on the subsetsof the patient's historical patient data associated with the 10 AMhourly interval (e.g., task 804). Here, it should be noted that in oneor more embodiments, the amount of active insulin or carbohydrates arenot necessarily required to be calculated for the 10 AM interval orinput to the LSTM cell 1002 since the active insulin, carbohydrates,and/or a proxy therefore may be obtained from a preceding LSTM cell andscaled, reduced, discarded, or otherwise adjusted according to the modelassociated with the LSTM cell 1002. The average glucose value 1005associated with the 10 AM interval (e.g., 115 mg/dL) is displayed on theforecast region 902 of the GUI display 900 in association with the 10 AMhourly interval. The forecasted 10 AM glucose value 1005 is also inputto the 11 AM hourly interval LSTM cell 1004 along with the predictedpatient behavior 1007 for the 11 AM hourly interval (e.g., the predictedamount of carbohydrates consumed, insulin delivered, exercise,medication, and the like for the patient during the 11 AM to 12 PM timeperiod). The LSTM cell 1004 calculates or otherwise determines anaverage glucose value 1009 associated with the 11 AM interval utilizingthe forecasting model for the 11 AM hourly interval that was determinedbased on the subsets of the patient's historical patient data associatedwith the 11 AM hourly interval (e.g., task 804). The forecasted glucosevalue 1009 for the 11 AM interval (e.g., 110 mg/dL) is displayed on theforecast region 902 of the GUI display 900 in association with the 11 AMhourly interval and input to the 12 PM hourly interval LSTM cell 1006for determining a forecasted glucose value for the 12 PM interval (e.g.,100 mg/dL), and so on.

In one or more exemplary embodiments, the forecasted glucose values inthe glucose forecast region 902 are displayed or otherwise rendered withvisually distinguishable characteristics that indicate the relationshipof an individual forecasted glucose value with respect to one or morethreshold values. For example, in the illustrated embodiment of FIG. 9,forecasted glucose values within a target range of glucose values forthe patient (e.g., between 80 mg/dL and 140 mg/dL) are rendered using avisually distinguishable characteristic that indicates those values arenormal, desirable, or otherwise acceptable (e.g., a green color), withforecasted glucose values outside the target range being rendered usinga different visually distinguishable characteristic that indicates thosevalues are potentially problematic or undesirable (e.g., a red color).In this regard, for the illustrated embodiment, the forecasted glucosevalues associated with the 4 PM and 5 PM hourly intervals in the glucoseforecast region 902 may be rendered in red to indicate they are below alower threshold value for the patient's target glucose range indicativeof a potential hypoglycemic event being forecasted for the patient at oraround those times, while the forecasted glucose values preceding 4 PMin the glucose forecast region 902 may be rendered in green to indicatethe patient's glucose is forecasted to be within the target range forthe next 6 hours.

In one or more embodiments, the glucose forecast region 902 isscrollable or otherwise adjustable to allow the patient or user to viewforecasted glucose values further into the future. For example, in oneor more embodiments, the glucose forecast region 902 is scrollable orotherwise adjustable to allow the patient or user to view forecastedglucose values for the next 24 hours. Additionally, it should be notedthat in some embodiments, the forecasted glucose values in the glucoseforecast region 902 may be dynamically updated in real-time in responseto changes to the patient's current sensor glucose measurement value,the current operational context, or other real-time behaviors oractivities by the patient.

Referring now to FIG. 11, in accordance with one or more embodiments, anensemble prediction process 1100 may be performed to determine anensemble prediction for the physiological condition of the patient as acombination of predicted values determined using a plurality ofdifferent prediction models. Since the different prediction models mayutilize different input variables, different prediction horizons, and/ordifferent formulas or techniques for determining future glucose values,the ensemble prediction of the patient's glucose level may betterreflect the potential variability in the patient's future glucose levelrather than reliance on any individual prediction model. In this regard,forecasted hourly glucose values determined in accordance with theforecasting process 800 may be weighted or otherwise combined withpredicted glucose values for the patient determined using otherprediction models to obtain an ensemble prediction of the patient'sglucose level with respect to time that reflects the relativereliability or accuracy of the respective prediction models with respectto time.

The various tasks performed in connection with the ensemble predictionprocess 1100 may be performed by hardware, firmware, software executedby processing circuitry, or any combination thereof. For illustrativepurposes, the following description refers to elements mentioned abovein connection with FIGS. 1 and 6-7. In practice, portions of theensemble prediction process 1100 may be performed by different elementsof a patient data management system 100 or an infusion system 600, suchas, for example, the server 102, the electronic device(s) 106, aninfusion device 602, and/or the pump control system 620, 700. It shouldbe appreciated that the ensemble prediction process 1100 may include anynumber of additional or alternative tasks, the tasks need not beperformed in the illustrated order and/or the tasks may be performedconcurrently, and/or the ensemble prediction process 1100 may beincorporated into a more comprehensive procedure or process havingadditional functionality not described in detail herein. Moreover, oneor more of the tasks shown and described in the context of FIG. 11 couldbe omitted from a practical embodiment of the ensemble predictionprocess 1100 as long as the intended overall functionality remainsintact.

The ensemble prediction process 1100 begins by retrieving or otherwiseobtaining historical data associated with a particular patient anddeveloping, training, or otherwise determining multiple differentpatient-specific glucose prediction models based on the patient'shistorical data (tasks 1102, 1104). In this regard, in addition todetermining a glucose forecasting model as described above in thecontext of the forecasting process 800, one or more additional modelsfor the patient may be determined based on the patient's historical data120 that predict the patient's future glucose levels in a different way(e.g., using a different algorithm or modeling technique, etc.), basedon different input variables, with a different level of temporalgranularity (e.g., on a minute-by-minute basis, an hourly basis, etc.),and/or the like. It should be noted that in practice there are numerousdifferent types of predictive models that could be utilized, and thesubject matter described herein is not intended to be limited to anyparticular type or combination of models, techniques, or methods used topredict glucose levels.

For example, in one or more embodiments, in addition to apatient-specific neural network-based forecasting model, anautoregressive integrated moving average (ARIMA) model for predictingfuture glucose levels is also determined using the patient's historicaldata. In this regard, the ARIMA model predicts future glucose levelsbased on cyclical patterns in the patient's historical data, which maybe correlated to different events and/or operational contexts. Inexemplary embodiments, the ARIMA model is configured to determinepredicted glucose values for the patient at increments in the futurecorresponding to the sampling rate associated with the glucose sensingarrangement 604 and/or the patient's historical sensor glucosemeasurement data. For example, in one embodiment, the glucose sensingarrangement 604 provides new or updated sensor glucose measurementvalues every 5 minutes, and the ARIMA model is configured to determinepredicted glucose values at 5 minute intervals into the future. In oneor more exemplary embodiments, machine learning or similar techniquesare utilized to determine which combination of historical delivery data,historical auxiliary measurement data, historical event log data,historical geolocation data, and other historical or contextual data arecorrelated to or predictive of the historical sensor glucose measurementdata, and then determines a corresponding ARIMA model for calculating orpredicting future sensor glucose measurement values based on that set ofinput variables and a preceding subset of historical sensor glucosemeasurement values. In this regard, the trajectory of the precedingsubset of historical sensor glucose measurement values in conjunctionwith concurrent or preceding events or operational contexts that arehistorically correlative to or predictive of changes in the patient'ssensor glucose level influence the predicted future sensor glucosemeasurement values determined using the model. In one embodiment, thetraining of the autoregressive component of the ARIMA model attempts toidentify the capability of the patient's glucose level to regress on itsown while the moving average component of the ARIMA model attempts tocompensate for a slow background shift in the patient's glucose levels.

In one or more embodiments, a patient-specific physiological model forpredicting future glucose levels is also determined using the patient'shistorical data in a manner that attempts to emulate the patient'spharmacodynamics and pharmacokinetics with compensation for inter- andintra-personal variance. Similar to the ARIMA model, thepatient-specific physiological model may be configured to determinepredicted glucose values for the patient at increments in the futurecorresponding to the sampling rate associated with the glucose sensingarrangement 604 and/or the patient's historical sensor glucosemeasurement data. That said, the patient-specific physiological modelmay determine the predicted glucose values in a different manner thanthe ARIMA model and/or based on different input variables than thoseused by the ARIMA model. For example, in one or more embodiments, one ormore patient-specific physiological parameters (e.g., glucose rate ofappearance, insulin action, and/or the like) are determined for thepatient based on relationships between the patient's historical sensorglucose measurement data, historical meal data, historical deliverydata, historical bolus data, and/or the like. The physiological modelutilizes the patient-specific physiological parameters to predict futuresensor glucose measurement values based on the patient's current orrecent sensor glucose measurement values, current insulin on board,recent meal data, and/or the like. In this regard, the output of thepatient-specific physiological parameters represents the expectedglucose levels for the patient based on the patient's historicalphysiological response given the current amount of insulin on boardand/or amount of carbohydrates yet to be metabolized by the patient.

Still referring to FIG. 11, in exemplary embodiments, after determiningor otherwise obtaining a plurality of different patient-specific glucoseprediction models, the ensemble prediction process 1100 continues byidentifying or otherwise obtaining the current operational context forthe patient and calculating or otherwise determining reliability metricsassociated with the different patient-specific glucose prediction modelsfor different prediction horizons in advance of the current time of daybased on the current operational context (tasks 1106, 1108). In thisregard, the current time of day, the current day of the week, thecurrent geographic location, the current network address and/or type ofnetwork connectivity, and/or other contextual data associated with adevice 106, 602 associated with the patient is identified or otherwiseobtained. Based on the current operational context, subsets of thepatient's historical data 120 corresponding to the current operationalcontext are obtained and utilized to determine one or more accuracy orreliability metrics associated with the different glucose predictionmodels. For example, if the current operational context indicates it is8 AM on a Wednesday and the patient is at home, prior subsets of thepatient's historical data 120 having associated timestamps at or around8 AM on Wednesdays and/or having associated geographic locationscorresponding to the patient's home geographic location may be obtainedand then utilized to determine the reliability of the different glucoseprediction models.

For each prediction model, the appropriate input variables are obtainedfrom the relevant subset of the patient's historical data, and thecalculated glucose values output by the model are compared to thepatient's historical sensor glucose measurement values at correspondingtimes to obtain a reliability metric for the model. For example, a meanabsolute difference, standard deviation, or other statisticalmeasurement may be calculated by comparing a set of output values from aglucose prediction model corresponding to a prediction horizon after apoint of time (e.g., the four hours of predicted glucose valuesfollowing the 8 AM reference point) to the corresponding historicalglucose measurement values (e.g., the four hours of historical patientsensor glucose measurement values following the 8 AM reference point ona Wednesday). In one or more embodiments, the reliability metrics aredetermined for hourly intervals, for example, by calculating the meanabsolute difference within the first hour after the prediction time(e.g., using values corresponding to the 8 AM to 9 AM timeframe), themean absolute difference within the second hour after the predictiontime (e.g., using values corresponding to the 9 AM to 10 AM timeframe),and so on. In this regard, the reliability metric associated with eachparticular prediction model may vary depending on the particularprediction horizon or time window in advance of the current predictiontime.

Based on the reliability metrics associated with the differentprediction models, the ensemble prediction process 1100 calculates orotherwise determines weighting factors to be associated with the outputsof the different patient-specific glucose prediction models fordifferent prediction horizons in advance of the current time of day andthen calculates or otherwise determines ensemble predicted glucosevalues within those different prediction horizons as weighted averagesof the outputs of the different patient-specific glucose predictionmodels using those weighting factors (tasks 1110, 1112). In this regard,based on the relationship between the reliability metrics across thedifferent patient-specific glucose prediction models for a particularprediction horizon, time window or sampling time, weighting factors maybe assigned to the different models accordingly to increase theinfluence of the more reliable model(s) on the ensemble predictedglucose values within that prediction horizon.

For example, if the patient's ARIMA model is fifty percent more reliablethan the patient's hourly forecasting model for the second hour inadvance of the current prediction time (e.g., the 9 AM to 10 AMtimeframe), the predicted glucose values output by the ARIMA model forthe second hour may be assigned a weighting factor that is fifty percenthigher than the weighting factor assigned to the hourly forecastingmodel. Ensemble predicted values for that prediction horizon (i.e., thesecond hour in advance of the current time) may then be determined asthe weighted average of the 5-minute predicted glucose values output bythe ARIMA model for that time frame (e.g., the 9 AM to 10 AM values) andthe hourly forecast glucose value output by the hourly forecast model(e.g., the forecasted average glucose level for the 9 AM to 10 AM timewindow), resulting in 5-minute ensemble predicted values that arecomposed of 60% of the ARIMA predicted glucose value at that particular5 minute sampling time (e.g., the ARIMA predicted glucose value for 9:05AM) and 40% of the hourly forecast glucose value for the predictionhorizon. However, for the third hour in advance of the currentprediction time, the patient's hourly forecasting model may be fiftypercent more reliable than the predicted glucose values output by theARIMA model for the third hour, resulting in the weighting factorassigned to the hourly forecasting model being fifty percent higher thanthat assigned to the ARIMA model, thereby resulting in 5-minute ensemblepredicted values that are composed of 40% of the ARIMA predicted glucosevalue at a particular 5 minute sampling time (e.g., the ARIMA predictedglucose value for 10:05 AM, 10:10 AM, and so on) and 60% of the hourlyforecast glucose value for the prediction horizon (e.g., the hourlyforecasted glucose value for the 10 AM to 11 AM time period).

After determining an ensemble glucose prediction into the future, theensemble prediction process 1100 continues by generating or otherwiseproviding a graphical representation of the ensemble predicted glucosevalues to the patient or other user (task 1114). For example, asdepicted in FIG. 12, a GUI display 1200 may be presented on a clientelectronic device 106 and/or infusion device 602 that includes agraphical representation 1202 of the patient's sensor glucosemeasurement data with respect to time preceding a marker 1206 or similargraphical indication of the current time, followed by a graphicalrepresentation 1204 of the ensemble predicted glucose values withrespect to time after the marker indicating the current time. In one ormore embodiments, the reliability metrics associated with the ARIMAmodel, physiological model, or other shorter term prediction modelsdecrease relative to the reliability metrics associated with the hourlyforecasting model as the prediction horizon advances further into thefuture in advance of the current time, such that the graphicalrepresentation of the ensemble predicted glucose values converge towardthe hourly forecast glucose levels as the patient or user scrolls,slides, or otherwise adjusts the GUI display 1200 to advance theprediction horizon associated with the displayed values. In this regard,scrolling or adjusting the sensor glucose measurement display region 904may result in updating the sensor glucose measurement display region 904to present the GUI display 1200 depicting the ensemble glucoseprediction 1204 extending into the future from the current time marker1206.

In one or more exemplary embodiments, the earlier portions of theensemble glucose prediction 1204 are weighted more heavily towardoutputs of prediction models that are more reliable in the short-termwhile portions of the ensemble glucose prediction 1204 further into thefuture are weighted more heavily toward outputs of prediction modelshaving better longer term reliability. For example, the portion of theensemble glucose prediction 1204 from 10 AM to 11 AM may be composed of60% of the output of the patient's ARIMA model and 40% of the patient'shourly forecasting model, while the subsequent portion of the ensembleglucose prediction 1204 from 11 AM to 12 PM may be composed of 40% ofthe output of the patient's ARIMA model and 60% of the patient's hourlyforecasting model, the portion of the ensemble glucose prediction 1204from 12 PM to 1 PM may be composed of 30% of the output of the patient'sARIMA model and 70% of the patient's hourly forecasting model, and soon.

FIG. 13 depicts an exemplary patient simulation process 1300 suitablefor implementation in connection with the ensemble prediction process1100 to simulate or otherwise predict how different events or actions bythe patient are likely to influence the patient's glucose levels in thefuture. The various tasks performed in connection with the patientsimulation process 1300 may be performed by hardware, firmware, softwareexecuted by processing circuitry, or any combination thereof. Forillustrative purposes, the following description refers to elementsmentioned above in connection with FIGS. 1 and 6-7. In practice,portions of the patient simulation process 1300 may be performed bydifferent elements of a patient data management system 100 or aninfusion system 600, such as, for example, the server 102, theelectronic device(s) 106, an infusion device 602, and/or the pumpcontrol system 620, 700. It should be appreciated that the patientsimulation process 1300 may include any number of additional oralternative tasks, the tasks need not be performed in the illustratedorder and/or the tasks may be performed concurrently, and/or the patientsimulation process 1300 may be incorporated into a more comprehensiveprocedure or process having additional functionality not described indetail herein. Moreover, one or more of the tasks shown and described inthe context of FIG. 13 could be omitted from a practical embodiment ofthe patient simulation process 1300 as long as the intended overallfunctionality remains intact.

The patient simulation process 1300 begins by receiving or otherwiseobtaining user input indicative of future events, actions or otheractivities for a patient (task 1302). In this regard, the patient oranother user may input or otherwise provide information thatcharacterizes, quantifies, or otherwise defines actions or events thatare anticipated, contemplated, or otherwise being considered by thepatient. For example, the user input may indicate a prospective amountof carbohydrates to be consumed by the patient, a prospective amount ofexercise to be performed by the patient, a prospective bolus amount ofinsulin to be administered by the patient, and/or the like.Additionally, the user input may indicate a time of day associated withthe prospective activities (e.g., an expected meal time for a futureamount of carbohydrates), a time window or duration of time of interest,or other temporal information characterizing the prospective activity bythe patient. In some events, the prediction engine 708 may automaticallypredict future actions or events and corresponding parameters orcriteria associated therewith based on the patient's historicalmeasurement data, event log data, contextual data, and/or the like. Insuch embodiments, the patient or another user may input or otherwiseprovide confirmation of the predicted future events, or otherwise adjustone or more characteristics associated with the predicted future events(e.g., adjusting the future timing, an amount, duration, type or othercharacter associated with the event, and/or the like).

The patient simulation process 1300 continues by calculating orotherwise determining adjusted weighting factors for combining outputfrom the patient's glucose prediction models into an ensemble predictionbased on the current operational context and the input future activityinformation (task 1304). In this regard, similar to as described abovein the context of the ensemble prediction process 1100, the patientsimulation process 1300 calculating or otherwise determining reliabilitymetrics associated with the different patient-specific glucoseprediction models for different prediction horizons in the future basedon the current time of day and other operational contexts in a mannerthat also accounts for the input future activity information.

In one or more embodiments, when selecting subsets of the patient'shistorical data 120 for determining reliability metrics, the patientsimulation process 1300 may exclude, from the subsets of the patient'shistorical data 120 corresponding to the current operational context,any subset of the patient's historical data 120 corresponding to thecurrent operational context that does not contain one or more of theinput future activities within the prediction horizon or otherwisewithin a threshold period of time from the current time of day. Forexample, if the current operational context indicates it is 8 AM on aWednesday and the patient is at home, and the user input indicates thepatient is intending to consume carbohydrates or otherwise experience ameal event within a threshold amount of time, only prior subsets of thepatient's historical data 120 having associated timestamps at or around8 AM on Wednesdays and/or having associated geographic locationscorresponding to the patient's home geographic location that are alsohave a contemporaneous or concurrent meal within a threshold amount oftime after 8 AM are selected for analysis. In this regard, varying thedata set used in calculating the reliability metrics associated with thedifferent prediction models may result in prospectively-adjustedreliability metric values associated with the respective predictionmodels that are different from the normal reliability metric values thatwould otherwise be associated with the respective prediction models inthe absence of accounting for future activity. The patient simulationprocess 1300 then determines prospectively-adjusted weighting factorsbased on the relationship between the adjusted reliability metricsacross the different patient-specific glucose prediction models. In asimilar manner as described above, the prospectively-adjusted weightingfactors may also vary with respect to the prediction time in advance ofthe current time.

Still referring to FIG. 13, after determining weighting factorsprospectively-adjusted to account for the input future activityinformation, the patient simulation process 1300 calculates or otherwisedetermines a prospective ensemble glucose prediction based on the inputfuture activity information using the prospectively-adjusted weightingfactors (task 1306). In this regard, the input future activityinformation is provided as an input to one or more of the patient'sglucose prediction models to thereby alter or influence the predictedglucose values output by the model(s) in a manner that accounts for theprescribed future event(s) at the corresponding time(s) in the future.For example, if the user input indicates the patient is likely to eat ameal at a particular time in the future, the input meal information isprovided as an input to the LSTM cell of the patient's hourlyforecasting model that encompasses or otherwise corresponds to that timein the future, thereby influencing the forecasted glucose value for thattime interval and/or subsequent time intervals. As another example, ifthe user input indicates the patient is likely to administer a bolus ofinsulin at the current time, the input bolus amount may be provided toeach of the patient's glucose prediction models in a manner thataccounts for the bolus amount of insulin upon initialization (e.g., byadding the input bolus amount to the current amount of active insulinassociated with the current time of day).

After predicted glucose values accounting for the prospective patientactivity are calculated using each of the patient's glucose predictionmodels, ensemble predicted values are determined as a weighted averageof the respective predicted glucose values output by the respectiveglucose prediction models using the prospectively-adjusted weightingfactors, in a similar manner as described above (e.g., task 1112). Byvirtue of prospectively adjusting the weighting factors as well asutilizing the future activity as input to the prediction models, theresulting ensemble predicted values effectively simulate or project thepatient's glucose level into the future if the patient engages in theinput activity. Accordingly, the prospective ensemble prediction mayalternatively be referred to herein as the patient's simulated glucoselevel.

In exemplary embodiments, the patient simulation process 1300 generatesor otherwise provides a graphical representation of the patient'ssimulated glucose level or other feedback that is influenced by theprospective ensemble glucose prediction (task 1308). For example, insome embodiments, a line chart or graph of the patient's simulatedglucose level may be presented in response to the input future activityinformation. In other embodiments, the simulated glucose values may beprocessed or otherwise analyzed to provide one or more recommendationsto the patient (e.g., indication of whether or not to engage in theinput activity, or the like).

For example, referring now to FIG. 14 with reference to FIG. 13, in oneor more embodiments, the patient simulation process 1300 is performed inconjunction with a conversational interaction with the patient that maybe supported by a conversational interaction application 712 at aninfusion device 602 or other client device 106. In the illustratedembodiment, the patient manipulates or otherwise interacts with theclient device 106, 602 to input that the patient would like to view hisor her simulated glucose levels 4 hours into the future if the patientcontemporaneously consumes 60 grams of carbohydrates and administers abolus of 3 units of insulin. In response to receiving the user input,the conversational interaction application 712 may provide the inputparameters to the prediction engine 708 for simulating the patient'sglucose levels in accordance with the patient simulation process 1300.In this regard, the patient simulation process 1300 determinesprospectively-adjusted weighting factors for the patient's predictionmodels based on the respective reliability metrics associated with themodels for the subsequent 4 hours after instances when the patienthistorically has consumed carbohydrates and/or administered a bolus ofinsulin at or around the current time of day. The patient simulationprocess 1300 then inputs or otherwise provides 60 grams of carbohydratesand 3 units of insulin to each of the patient's prediction models toinitialize the models as if the carbohydrates are being consumed and theinsulin is being delivered contemporaneously or otherwise upon startupof the model. Thereafter, predicted glucose values for the patient arecalculated for the next 4 hours into the future using the patient'sprediction models initialized with 60 grams of carbohydrates and 3 unitsof insulin. Prospective ensemble predicted glucose values for the next 4hours are then determined as a weighted average of the predicted glucosevalues accounting for the contemporaneous intake of carbohydrates andinsulin using the prospectively-adjusted weighting factors.

After determining the prospective ensemble predicted glucose values forthe patient, the prediction engine 708 may provide the prospectiveensemble predicted glucose values to the conversational interactionapplication 712 for presentation to the patient within the context ofthe ongoing conversational interaction. In this regard, the conversationGUI display 1400 including a graphical representation 1402 of the userinput is updated to include a conversational response 1404 to the userinput that is influenced by the simulated glucose values. In theillustrated embodiment, the conversational response 1404 includes agraphical representation 1406 of the patient's simulated glucose level(e.g., a line chart of the prospective ensemble predicted glucosevalues) for the next 4 hours following a marker 1408 indicating thecurrent time of day.

FIG. 15 depicts another exemplary GUI display 1500 depicting aconversational interaction that may incorporate the ensemble predictionprocess 1100 of FIG. 11 and/or the patient simulation process 1300 ofFIG. 13. In the illustrated embodiment, the conversational interactionapplication 712 receives an initial user input 1502 and analyzes theinitial user input to determine the patient is interested in aprediction of his or her physiological condition. The conversationalinteraction application 712 generates or otherwise provides aconversational response 1504 that prompts the patient to input orotherwise provide an indication of a prediction horizon and/orpotentially other parameter for the prediction to be performed. Forexample, in some embodiments, the conversational interaction application712 may prompt the patient to provide input of any anticipatedactivities within the input prediction horizon for prospectivelyadjusting the prediction in accordance with the patient simulationprocess 1300.

In response to receiving a subsequent user input 1506 indicating thatthe patient is interested in a prediction over the next 12 hours, theconversational interaction application 712 commands, signals, orotherwise instructs the prediction engine 708 to predict the patient'sglucose level for the next 12 hours. The prediction engine 708 performsthe ensemble prediction process 1100 to calculate or otherwise determineensemble predicted glucose values for the patient over the next 12 hoursbased on the patient's current or recent glucose measurements, currentactive insulin, the current operational context, and/or the like asdescribed above. In the illustrated embodiment, the prediction engine708 also utilizes the reliability metrics associated with the respectiveprediction models (e.g., standard deviations, mean absolute differences,and/or the like) to probabilistically determine the likelihood of one ormore physiological events (e.g., a hypoglycemic event, a hyperglycemicevent, and/or the like) within the prediction horizon based on theensemble predicted glucose values. The prediction engine 708 providesthe ensemble predicted glucose values and corresponding physiologicalevent probabilities to the conversational interaction application 712,which generates a conversational response 1508 providing feedbackinfluenced by the patient's ensemble predicted values. For example, theillustrated conversational response 1508 includes graphicalrepresentations of hypoglycemic event probabilities with respect todifferent intervals within the prediction horizon along with anindication of when the probability of a hypoglycemic event based on thecurrent time of day.

In the illustrated embodiment, the conversational response 1508 alsoprompts the patient for whether or not the patient could like toconfigure one or more settings at the device 106, 602 based on thepredicted glucose levels. In response to receiving a user input 1510indicating a desire to configure a reminder, the conversationalinteraction application 712 may configure itself to generate orotherwise provide a reminder at the time of day when the probability ofa hypoglycemic event is highest based on the ensemble predicted valuesand reliability metrics and then generate or otherwise provide aconversational response 1512 confirming or otherwise indicating thereminder has been set.

FIG. 16 depicts another exemplary GUI display 1600 depicting aconversational interaction that may incorporate one or more of theprocesses 300, 800, 1100, 1300 described above. In the illustratedembodiment, the conversational interaction application 712 receives aninitial conversational user input 1602 and analyzes the initial userinput to determine the patient is interested in a prediction of his orher physiological condition in response to a future exercise event. Theconversational interaction application 712 generates or otherwiseprovides a sequence of conversational responses 1604, 1608, 1612 thatprompts the patient to conversationally input 1606, 1610, 1614 anddefine anticipated attributes for the future exercise event, such as,the anticipated type of event, the anticipated duration of the event,and the anticipated timing of the event. After the attributes of thefuture event are defined, the prediction engine 708 performs the patientsimulation process 1300 to calculate or otherwise determine aprospective glucose level for the patient after the event (e.g., at atime corresponding to a sum of the input timing 1614 for the event andthe input duration 1610 for the event) based on the patient's currentglucose measurement, current active insulin, the current operationalcontext, with the attributes associated with the future event beinginput or otherwise provided to the patient's forecasting and predictionmodels in accordance with the anticipated timing input by the patent.

In some embodiments, to adjust the model weighting factors, reliabilitymetrics, or other aspects of the patient simulation process 1300 toaccount for the prospective patient activity, the querying process 300may be performed to obtain data or information characterizing theresponses of other similar patients to the prospective future event. Forexample, the querying process 300 may be performed to identify similarpatients based on common links or edges between nodes within a logicaldatabase layer and obtain historical measurement data for those similarpatients' glycemic responses to the input type of activity for the inputduration at the input time of day (e.g., sensor glucose measurement datafor similar patients when jogging at 10 AM for the following 30minutes). The average or typical physiological response by similarpatients may then be utilized to adjust or otherwise augment theindividual patient's physiological prediction model, which, in turn isthen utilized by the ensemble prediction process 1100 and/or the patientsimulation process 1300 to obtain an prospective ensemble glucoseprediction that is influenced by the patient's hourly glucose forecastsaccounting for the input future exercise (e.g., by inputting theexercise attributes to the 10 AM LSTM unit) in combination with theadjusted physiological prediction for the patient's glucose level.

As another example, a patient may conversationally interact with aclient device 106, 602 to obtain a prediction of what his or her sensorglucose level is likely to be upon waking up in the morning. Based onthe patient's historical event log data, an estimated sleep durationand/or estimated wake up time for the patient may be determined, andwhich may be utilized to adjust the model weighting factors and beprovided as input to the patient's prediction models to obtain aprospective ensemble prediction of the patient's glucose level at oraround the estimated wake up time. In one or more embodiments, theprospective ensemble glucose prediction is also utilized to generate oneor more recommendations to the patient. For example, if the prospectiveensemble glucose prediction at the estimated wakeup time is outside ofthe target range of glucose values, the querying process 300 may beperformed to identify actions performed by similar patients at or beforebed time or otherwise associated with the overnight period that resultedin a change in those patients' glucose levels that, if a correspondingincrease or decrease occurred with respect to the current patient, wouldresult in the prospective ensemble glucose prediction at the estimatedwakeup time being within the target range. In this regard, the queryingprocess 300 may be utilized to identify a recommended amount ofcarbohydrates the patient should eat, a recommended amount of insulinthe patient should bolus, and/or a recommended amount of exercise thatthe patient should perform prior to sleeping to achieve a desiredglucose level upon waking. If the prospective ensemble glucoseprediction at the estimated wakeup time is within the target range ofglucose values, other recommendations that are likely to improve thepatient's glucose regulation (e.g., increase the percentage of the daywithin the target glucose range, minimize glucose excursion events,and/or the like) may be determined based on similar patients andprovided to the patient, such as, for example, a recommended duration ofsleep, a recommended amount of carbohydrates for the following day, arecommended amount of exercise for the following day, and/or the like.

Referring to FIGS. 8-16 and with reference to FIG. 1, in someembodiments, one or more of the processes 800, 1100, 1300 may beimplemented in connection with the patient data management system 100and adapted to leverage the graph data structures in the database 104 toimprove the accuracy of the modeling and resulting predictions. In thisregard, weighted directional or causal links between nodes or entitiesmay be utilized to identify predictive relationships and correspondinginfluences on patient outcomes for improved modeling, while suchrelationships could otherwise be indeterminable or computationallyimpractical using conventional databases reliant on tables that lackcausal and/or probabilistic relationships between entities.

Prospective Therapy Management

Referring now to FIG. 17, in accordance with one or more embodiments, arisk management process 1700 utilizes measurement data pertaining to apatient's physiological condition in conjunction with the patient'smedical records data to calculate or otherwise determine a metricindicative of the patient's risk of experiencing a particular condition.For example, a patient's sensor glucose measurement data or a metriccalculated based thereon may be utilized in conjunction with a subset ofthe patient's medical records data to calculate or otherwise determine ametric indicative of how at risk the patient is for experiencing one ormore acute diabetic crises (e.g., severe hypoglycemia, acute diabeticketoacidosis, hyperosmolarity, and/or the like) and/or long-termcomplications. In exemplary embodiments, the risk management process1700 generates or otherwise provides notifications or recommendationspertaining to a condition the patient is at risk of to an end user(e.g., the patient, the patient's healthcare provider, the patient'scare partner, and/or the like). For purposes of explanation, the subjectmatter may be described herein in the context of notifications orrecommendations being provided to the patient, however, it should beappreciated that the subject matter described herein is not limited tothe type of end user to whom the notifications or recommendations arebeing provided. In some embodiments, one or more therapy recommendationsare provided to the patient in accordance with one or more of theprocess 1800 and/or the process 1900, as described in greater detailbelow in the context of FIGS. 18-19. Additionally, in the illustratedembodiment of FIG. 17, the value of the metric indicating the patient'slevel of risk for a particular condition may be utilized to adjust,modify, or otherwise influence the delivery of fluid by an infusiondevice 602 associated with the patient and/or otherwise alter thepatient's therapy.

The various tasks performed in connection with the risk managementprocess 1700 may be performed by hardware, firmware, software executedby processing circuitry, or any combination thereof. For illustrativepurposes, the following description refers to elements mentioned abovein connection with FIGS. 1 and 6-7. In practice, portions of the riskmanagement process 1700 may be performed by different elements of apatient data management system 100 or an infusion system 600, such as,for example, the server 102, the electronic device(s) 106, an infusiondevice 602, and/or the pump control system 620, 700. It should beappreciated that the risk management process 1700 may include any numberof additional or alternative tasks, the tasks need not be performed inthe illustrated order and/or the tasks may be performed concurrently,and/or the risk management process 1700 may be incorporated into a morecomprehensive procedure or process having additional functionality notdescribed in detail herein. Moreover, one or more of the tasks shown anddescribed in the context of FIG. 17 could be omitted from a practicalembodiment of the risk management process 1700 as long as the intendedoverall functionality remains intact.

In the illustrated embodiment, the risk management process 1700 beginsby receiving or otherwise obtaining measurement data and medical recordsdata for a patient population (tasks 1702, 1704). For example, theserver 102 may retrieve, from the database 104, a subset of historicalpatient data 120 for a population of patients and a corresponding subsetof the electronic medical records data 122 for that patient population.In one embodiment, historical patient data 120 and electronic medicalrecords data 122 are obtained for all common patients across data sets.However, in other embodiments, the patient population may be tailoredfor a particular demographic or combination of demographic attributes(e.g., by age, gender, income, and/or the like).

After obtaining measurement data and medical records data for a patientpopulation, the risk management process 1700 determines a risk model fora particular condition based on relationships between the measurementdata and the medical records data across the patient population (task1706). In exemplary embodiments, stepwise feature selection, such asrecursive feature elimination, is performed to identify which fields orattributes of the patient measurement data and medical records data aremost correlative to or predictive of the occurrence of a particularcondition within the patient population.

For example, the server 102 may analyze the historical sensor glucosemeasurement data for the patient population to identify which sensorglucose metrics (e.g., mean sensor glucose measurement value, sensorglucose measurement standard deviation, overnight mean sensor glucosemeasurement value, percentage of time the sensor glucose measurementvalue is within range, percentage of time the sensor glucose measurementvalue is above a hyperglycemia threshold, percentage of time the sensorglucose measurement value is below a hypoglycemia threshold, etc.) forsome subset of the patient population are predictive of or correlativeto the occurrence of a particular medical diagnosis code within theelectronic medical records for that subset of the patient population. Inthis regard, for a given medical diagnosis code of interest (e.g.,hypoglycemia, diabetic ketoacidosis, hyperosmolarity, cardiovasculardisease, and/or the like), the server 102 may perform stepwise featureselection across of the different sensor glucose measurement metricsassociated with the population patients to identify or otherwisedetermine a subset of the sensor glucose measurement metrics that arecorrelative to or predictive of occurrence of that medical condition'sdiagnostic code within the electronic medical records data 122.Similarly, for the medical condition of interest, the server 102 mayanalyze the electronic medical records data for the patient populationby performing stepwise feature selection to identify which fields orattributes of the patient medical records (e.g., age, gender, income,education level, smoking, A1C values or other laboratory values, insulinstatus or other medications or therapies, other medical conditions,and/or the like) are correlative to or predictive of occurrence of thatmedical condition. It should be noted that in some embodiments,operating context data for the patient population may also be analyzedto identify whether any particular operating contexts (e.g., geographiclocation, temperature, humidity, and/or the like) are correlative to orpredictive of occurrence of a particular medical condition.

After identifying the sensor glucose measurement variables and medicalrecord variables that are correlative to or predictive of occurrence ofa medical condition, the server 102 then calculates or otherwisedetermines an equation, function, or model for calculating theprobability or likelihood of the occurrence of the medical condition ofinterest based on that predictive subset of sensor glucose measurementvariables and medical record variables. For example, a risk predictionmodel for cardiovascular disease may calculate the probability of apatient developing cardiovascular disease in the future based on thepatient's mean sensor glucose measurement value, sensor glucosemeasurement standard deviation, the percentage of time the patient'ssensor glucose measurement value is outside of a target range, patientage, and whether or not the patient is on insulin therapy. Depending onthe embodiment, a risk prediction model could calculate a riskprobability within a limited future prediction horizon (e.g., within thenext 18 months, within the patient's life expectancy, and/or the like)or for an unlimited or unbounded duration of time. After determiningrisk prediction models for various medical conditions and/or patientpopulations, the server 102 may store or otherwise maintain the riskprediction models for the different medical conditions in the database104 in association with the patient population demographic criteria forthe respective model. In other embodiments, the server 102 may transmitor push the risk prediction models to one or more client electronicdevices 106, 602. In this regard, in some embodiments, the server 102may periodically update the risk prediction models (e.g., weekly,monthly, yearly, and/or the like) to reflect new or more recent data inthe database 104.

Still referring to FIG. 17, the illustrated risk management process 1700receives or otherwise obtains measurement data and medical records datafor an individual patient and applies one or more risk prediction modelsto the patient's measurement data and medical records data to determinethe patient's individual risk of experiencing the condition(s)associated with the respective risk prediction model(s) (tasks 1708,1710, 1712). In this regard, an individual patient's sensor glucosemeasurement data and electronics medical records data may beperiodically or continually analyzed using the risk prediction models toascertain whether the patient's risk of a particular medical conditionis above a threshold risk tolerance. In one or more exemplaryembodiments, an individual patient's risk for particular conditions isanalyzed or otherwise determined at a client device 106, 602 associatedwith the patient. In this regard, the client device 106, 602 maydownload or otherwise retrieve, from the database 104 via the server102, risk prediction models for conditions that its associated patientdoes not have or has not been diagnosed. The demographic information andmedical records data associated with the patient may be utilized toidentify which patient population(s) the patient belongs to and thenselect risk prediction models associated with the identifier patientpopulation(s) for the medical conditions that do not have diagnosiscodes present in the patient's medical records data.

After obtaining a risk prediction model for a particular medicalcondition, the client device 106, 602 utilizes the current or recentsensor glucose measurement data associated with the patient to calculateor otherwise determine one or more inputs to the risk prediction model.Additionally, the client device 106, 602 may download or otherwiseretrieve, from the database 104 via the server 102, the field(s) of thepatient's medical records data that are also utilized as inputs to therisk prediction model. The client device 106, 602 then utilizes theequation, formula, or function associated with the risk prediction modelto calculate or otherwise determine, based on the patient's recentmeasurement data and medical record fields, an output value representingthe patient's probability of developing or experiencing the medicalcondition associated with the risk prediction model, that is, thepatient's risk score for that condition. Additionally, in embodimentswhere the risk prediction model utilizes contextual information as aninput, the client device 106, 602 may obtain the current operatingcontext via one or more sensing arrangements 650, 660 at the clientdevice 106, 602 and input the current operating context to the riskprediction model.

In exemplary embodiments, when the patient's risk score is greater thana notification threshold, the risk management process 1700 generates orotherwise provides a user notification that indicates the potential riskto the patient (tasks 1714, 1716). For example, a user notification maybe generated or otherwise provided at the client device 106, 602 thatidentifies the medical condition that the patient may be at risk ofexperiencing or exhibiting. In some embodiments, the risk managementprocess 1700 generates or otherwise provides a therapy recommendationbased on the medical condition. In this regard, the patient'smeasurement data and/or medical records data input to the riskprediction model may be analyzed to identify or otherwise determinewhether any of the input variables are capable of being modified todecrease the patient's risk score and provide recommended remedialactions to the patient. For example, a GUI display may be generated atthe client device 106, 602 that includes recommended actions that thepatient could take (e.g., exercise, dietary changes, etc.) to lower hisor her mean sensor glucose measurement value when a higher sensorglucose measurement value is predictive of a particular medicalcondition. As another example, a GUI display at the client device 106,602 may include recommended therapy changes (e.g., changing therapytypes, adding a new medication, and/or the like). In this regard, therisk management process 1700 may initiate the process 1800 describedbelow to identify which therapy modifications should be recommended tothe patient to achieve a desired reduction in the patient's risk score.

Still referring to FIG. 17, in one or more exemplary embodiments, therisk management process 1700 adjusts or modifies delivery of fluid by aninfusion device based at least in part on the patient's risk score for aparticular medical condition (task 1718). In this regard, based on themagnitude of the patient's risk score(s) and/or the medicalcondition(s), the command generation application 710 may adjust one ormore delivery commands to compensate for the patient's risk. Forexample, when the patient's risk score indicates that the patient's riskof a severe hypoglycemic event is greater than a threshold probability,the command generation application 710 may decrease delivery commands tomitigate the risk of a hypoglycemic event. Thus, even though thepatient's current sensor glucose measurement value or predicted glucoselevels based on preceding measurement values or trends are above ahypoglycemic threshold or otherwise expected to remain within a targetrange of glucose values, the command generation application 710 maydecrease insulin delivery (e.g., by scaling down or decreasing deliverycommands, increasing the patient's target glucose level, increasing thepatient's insulin sensitivity factor, and/or the like) to proactivelyaccount for a heightened risk of hypoglycemia. As another example, whenthe patient's risk score indicates that the patient's risk of diabeticketoacidosis or another acute hyperglycemic event is greater than athreshold probability, the command generation application 710 mayincrease delivery commands, decrease the patient's insulin sensitivityfactor, and/or decrease the patient's target glucose value to mitigatethe risk by increasing the patient's insulin on board. Thus, even thoughthe patient's current sensor glucose measurement value or predictedglucose levels are below a hyperglycemic threshold or otherwise expectedto remain within a target range of glucose values, the commandgeneration application 710 may increase insulin delivery to proactivelydecrease the patient's risk of a hyperglycemic event. In someembodiments, the risk management process 1700 may dynamically determinerisk scores in real-time in response to new or updated sensor glucosemeasurement values and cease modifying delivery commands once thepatient's risk for a particular condition falls below a threshold value.

Referring now to FIG. 18, in one or more exemplary embodiments, anuplift recommendation process 1800 is performed to identify a therapyrecommendation that is likely to provide the most beneficial impact on apatient's physiological condition based on that patient's historicaldata (e.g., measurement data, event log data, contextual data, and/orthe like) and medical records data. In some embodiments, the upliftrecommendation process 1800 may identify what therapy change orintervention is likely to have the largest impact on an aspect of anindividual's physiological condition. In other embodiments, acost-benefit analysis or similar optimization technique is applied usingan uplift metric in conjunction with cost, adherence, patient burden,and/or other metrics to identify an optimal therapy recommendation forthe patient. It should be noted that although the terminology uplift,uplift modeling, and variants thereof may be utilized for purposes ofexplanation, the subject matter is not limited to uplift modeling. Thus,absent clear indication otherwise, uplift modeling should be understoodas encompassing any sort of incremental modeling of the impact of aparticular event or action on a particular outcome, including true liftmodeling, net lift modeling, and variants thereof.

The various tasks performed in connection with the uplift recommendationprocess 1800 may be performed by hardware, firmware, software executedby processing circuitry, or any combination thereof. For illustrativepurposes, the following description refers to elements mentioned abovein connection with FIGS. 1 and 6-7. In practice, portions of the upliftrecommendation process 1800 may be performed by different elements of apatient data management system 100 or an infusion system 600, such as,for example, the server 102, the electronic device(s) 106, an infusiondevice 602, and/or the pump control system 620, 700. It should beappreciated that the uplift recommendation process 1800 may include anynumber of additional or alternative tasks, the tasks need not beperformed in the illustrated order and/or the tasks may be performedconcurrently, and/or the uplift recommendation process 1800 may beincorporated into a more comprehensive procedure or process havingadditional functionality not described in detail herein. Moreover, oneor more of the tasks shown and described in the context of FIG. 18 couldbe omitted from a practical embodiment of the uplift recommendationprocess 1800 as long as the intended overall functionality remainsintact.

The uplift recommendation process 1800 receives or otherwise obtainshistorical patient data and medical records data for a patientpopulation, and then analyzes the relationships between the historicalpatient data and the medical records data to identify different patientgroups for modeling the impact on the patients' physiological conditionfor different therapy interventions (tasks 1802, 1804, 1806). Forexample, the server 102 may retrieve historical patient data 120 andelectronic medical records data 122 from the database 104 and thenutilize machine learning to identify cohorts of patients where differenttherapy interventions or changes have a statistically significantimprovement to an aspect of the physiological patients within thatpatient cohort, such as, for example, a reduction in A1C laboratoryvalues, a reduction in glucose excursion events, an increase in thepercentage of time sensor glucose measurements are within a targetrange, and/or the like. In this regard, the patient cohorts may bedefined by common demographic attributes (e.g., gender, income, and/orthe like), common medical diagnoses, common therapy regimens or therapytypes (e.g., monotherapy patients, dual therapy patients, etc.), commonmedications or prescriptions, and/or other medical recordscommonalities. For example, in addition to defining patient cohortsdemographically (e.g., by age, location, race, gender, socioeconomicstatus, profession, etc.), patient cohorts may be characterized ordefined utilizing clustering techniques to classify similar patientsusing other available data sets, such as, for example, mood logs,program interactions, personal goals, and/or the like, which, forexample, may be tracked, monitored or logged by an application at aclient device 106.

After identifying different patient groups for modeling for differenttherapy interventions, the uplift recommendation process 1800 determinesan uplift model for calculating an impact of the respective therapyintervention on the respective patient group (task 1808). In thisregard, the server 102 identifies the sensor glucose measurementvariables, medical record variables, and/or operating context variablesthat are correlative to or predictive of the improvement in thephysiological condition and then calculates or otherwise determines anequation, function, or model for calculating the likely improvement inthe physiological condition based on the identified subset of variables.For example, stepwise feature selection may be performed to identifywhich fields or attributes of patient measurement data and medicalrecords data are most correlative to or predictive of the amount of A1Creduction within the patient cohort. An uplift model for calculating theestimated A1C reduction for patients within that particular patientcohort may then be determined as a function of the correlative subset ofsensor glucose measurement variables, medical record variables, and/oroperating context variables. In this regard, for each of the differentpatient cohorts identified for different potential therapyinterventions, the server 102 may determine an uplift model forcalculating a metric indicative of the impact of the respective therapyintervention on the respective cohort patients' physiological conditionas a function of a subset of sensor glucose measurement variables,medical record variables, and/or operating context variables. The upliftmodels determined by the server 102 may be stored or otherwisemaintained in the database 104 in association with the respectivecombination of patient cohort attributes and therapy intervention. Insome embodiments, the server 102 may push or otherwise transmit touplift models to client devices 106, 602 associated with patientsclassified within the respective patient cohorts. It should be notedthat the uplift modeling is not limited to stepwise feature selection,and in other embodiments, random forests analysis, logistic regression,and/or other machine learning or artificial intelligence techniques maybe utilized to generate an uplift model.

Still referring to FIG. 18, to determine a therapy recommendation for anindividual patient, the uplift recommendation process 1800 receives orotherwise obtains the historical observational patient data and medicalrecords data for an individual patient and then identifies or otherwiseobtains the uplift models associated with patient groups that includethe patient or that the patient would otherwise be classified into basedon the patient's demographic information, medical records, and/or thelike (tasks 1810, 1812, 1814). In other words, the patient's medicalrecords, measurement data, event log data, and/or current operatingcontext may be utilized to identify which uplift models in the database104 are likely to be most relevant to the individual patient beinganalyzed. Thereafter, the uplift recommendation process 1800 calculatesor otherwise determines the impact or uplift metric associated with eachrespective therapy intervention for the patient based on the patient'smeasurement data and medical records data and the respective upliftmodels associated with the different therapy interventions (task 1816).In this regard, for each potential therapy intervention, the upliftrecommendation process 1800 may calculate or otherwise determine anestimated A1C reduction or other estimation of the uplift or impactassociated with the respective therapy intervention on the patient basedon the patient's medical records, measurement data, and/or currentoperating context.

After determining uplift metrics for different potential therapyinterventions for the patient, the uplift recommendation process 1800determines a therapy intervention recommendation based on the upliftmetrics and generating or otherwise providing indication of therecommended therapy intervention to the patient (tasks 1818, 1820). Forexample, in one embodiment, the uplift recommendation process 1800identifies the therapy intervention having the maximum estimated impactor benefit (e.g., the largest estimated A1C reduction) as therecommended therapy intervention for the patient. In other embodiments,the uplift recommendation process 1800 performs a cost-benefit analysisor other optimization to identify an optimal therapy intervention basedon the estimated uplift values associated with the different potentialtherapy interventions and the costs associated with the respectivepotential therapy interventions. For example, the uplift recommendationprocess 1800 identifies the therapy intervention having the highestratio of estimated uplift value to cost as the recommended therapyintervention. In this regard, in some embodiments, the claims data 124maintained in the database 104 may be utilized to calculate or otherwisedetermine an estimated cost associated with a particular therapyintervention, which, in turn, may be utilized to determine the relativeimpact or benefit of that therapy intervention (e.g., by dividing theuplift value by the estimated cost).

In one or more embodiments, the uplift recommendation process 1800identifies or otherwise determines an optimal therapy intervention basedon the estimated uplift values associated with the different potentialtherapy interventions, the costs associated with the different potentialtherapy interventions, and estimated adherence metric values associatedwith the different potential therapy interventions. In this regard, someembodiments of the uplift recommendation process 1800 may calculate orotherwise determine an adherence metric value representative of howlikely the patient is to adhere to the particular therapy intervention,as described in greater detail below in the context of the adherencerecommendation process 1900 of FIG. 19. Thus, in some embodiments, theuplift recommendation process 1800 may identify, for recommendation asthe optimal therapy intervention, a therapy intervention that does nothave the highest estimated uplift value but has a relatively lower costand/or higher adherence than one or more therapy interventions havingthe higher estimated uplift values. Thus, the patient may be apprised ofthe therapy intervention that is most cost effective and more likely tobe successful based on the patient's likelihood of adherence to therecommended therapy.

Referring now to FIG. 19, in one or more exemplary embodiments, anadherence recommendation process 1900 may be performed to identify atherapy recommendation that an individual patient is most likely toadhere to or will otherwise yield the highest adherence. In this regard,in exemplary embodiments described herein, adherence modeling isutilized to determine adherence metrics that represent the respectiveprobabilities that a patient with adhere to a particular therapyregimen, for example, by taking a fully prescribed therapy regimen orengaging in some other action as prescribed by the respective therapyregimen or indicative of an attempt to fulfill the respective therapyregimen (e.g., filling a prescription within a threshold amount of timeafter being written). Using the adherence metrics, a therapyintervention that is likely to have better adherence by the patient (andthereby, more likely to have a beneficial outcome relative toprescribing a therapy regimen that is unlikely to have that level ofadherence) may be recommended to the patient. For example, for a givenpatient, if the adherence metric value associated with an injectableinsulin regimen (e.g., 15%) is lower relative to the adherence metricfor an oral medication such as GLP-2 or Sulfonylurea (e.g., 50%), theoral medication may be recommended since it may be likely to providegreater uplift when its associated adherence probability is accountedfor.

The various tasks performed in connection with the adherencerecommendation process 1900 may be performed by hardware, firmware,software executed by processing circuitry, or any combination thereof.For illustrative purposes, the following description refers to elementsmentioned above in connection with FIGS. 1 and 6-7. In practice,portions of the adherence recommendation process 1900 may be performedby different elements of a patient data management system 100 or aninfusion system 600, such as, for example, the server 102, theelectronic device(s) 106, an infusion device 602, and/or the pumpcontrol system 620, 700. It should be appreciated that the adherencerecommendation process 1900 may include any number of additional oralternative tasks, the tasks need not be performed in the illustratedorder and/or the tasks may be performed concurrently, and/or theadherence recommendation process 1900 may be incorporated into a morecomprehensive procedure or process having additional functionality notdescribed in detail herein. Moreover, one or more of the tasks shown anddescribed in the context of FIG. 19 could be omitted from a practicalembodiment of the adherence recommendation process 1900 as long as theintended overall functionality remains intact.

The adherence recommendation process 1900 receives or otherwise obtainshistorical observational data, medical records data, and medical claimsdata for a patient population from a database (tasks 1902, 1904, 1906).The adherence recommendation process 1900 calculates or otherwisedetermines adherence metrics for different therapy interventions orregimens based on the relationships between the historical observationaldata, medical records data, and medical claims data for a patientpopulation (task 1908). For example, for each patient having his or hercorresponding medical records and medical claims data stored in thedatabase 104, the server 102 may analyze the relationship between thepatient's prescriptions and other therapy information from the patient'smedical records data and the number and/or frequency of the patient'smedical claims corresponding to those prescriptions or therapies in thepatient's claims data to determine an adherence metric for thatrespective therapy associated with the patient based on how well thepatient's claims data adheres to or aligns with the patient's prescribedtherapy. In this regard, patients having claims data indicating thatprescriptions are being filled with the prescribed frequency or withrelatively little delay after the prescriptions are written may beassigned relatively high adherence values, while patients whose claimsdata indicate prescriptions are not being filled regularly or promptlymay be assigned relatively low adherence values. Additionally, in someembodiments, the event log data or other observational patient data mayalso be utilized when determining adherence metrics. For example, thepatient's event log data may indicate when the patient takes aprescribed medicine and the corresponding dosage, which, in turn may becompared to the prescription information from the patient's medicalrecords data to determine how well the patient's behavior adheres to thepatient's prescribed therapy.

In the illustrated embodiment, after determining adherence metricsassociated with different therapies for different patients, theadherence recommendation process 1900 continues by analyzing therelationships between the observational patient data, the medicalrecords, the claims data, and the adherence values to determineadherence models for calculating an adherence metric for differenttherapies based on an individual patient's observational data, medicalrecords data, and claims data (task 1910). In this regard, for a subsetof patients having a particular therapy regimen in common, the server102 identifies the observational patient variables (e.g., sensor glucosemeasurement variables, meals, exercise, or other event log variables,operating context variables and/or the like), medical record variables(e.g., demographic information, medical conditions, and/or claims datavariables (e.g., refill data for previous prescriptions, and/or thelike) that are correlative to or predictive of the patients' adherencemetric values for that therapy regimen, and then calculates or otherwisedetermines an equation, function, or model for calculating the likelyadherence metric value for a given patient based on the identifiedsubset of variables associated with that prospective patient. Forexample, stepwise feature selection or other machine learning techniquesmay be performed to identify which fields or attributes of thehistorical observational patient data 120 and medical records data 122are most correlative to or predictive of the adherence metric valueamong patients prescribed a respective therapy regimen. An adherencemodel for calculating the estimated adherence for patients not currentlyon that therapy regimen may then be determined as a function of thecorrelative subset of variables. In this regard, for each of thedifferent potential therapy regimens or interventions, the server 102may determine an adherence model for calculating a metric indicative ofthe likely adherence to the respective therapy regimen based on existingpatients that are or were prescribed that respective therapy regimen.The adherence models determined by the server 102 may be stored orotherwise maintained in the database 104 in association with therespective therapy regimens or interventions or pushed or otherwisetransmitted to client devices 106, 602.

Still referring to FIG. 19, to determine a therapy recommendation for anindividual patient, the adherence recommendation process 1900 receivesor otherwise obtains observational data, medical records data, andclaims data for an individual patient and then applies the variousadherence models for different therapy regimen that are not currentlyprescribed to the patient to calculate or otherwise determine adherencemetrics for the different therapy regimens (tasks 1912, 1914). In thisregard, the server 102 or client device 106, 602 obtains data associatedwith a patient of interest from the database 104 along with recentmeasurement data and/or operational context information from a clientdevice 106, 602 associated with the patient, and then utilizes theadherence models to estimate that patient's likely adherence for each ofthe different potential therapy regimen that are not currentlyprescribed for the patient.

In exemplary embodiments, the adherence recommendation process 1900determines a therapy recommendation for the patient based on theadherence metric values associated with the different potential therapyregimen and generates or otherwise provides indication of the therapyrecommendation to the patient or another user (e.g., a physician, ahealthcare provider, and/or the like) (tasks 1916, 1918). In someembodiments, the adherence recommendation process 1900 selects orotherwise identifies the therapy regimen having the highest adherencemetric value as the recommended therapy for the patient. In otherembodiments, the adherence metric values associated with the differentpotential therapy regimens are considered in conjunction with upliftmetric values and/or estimated costs associated with the differentpotential therapy regimens to identify an optimal therapy regimen, asdescribed above in the context of FIG. 19. For example, in embodimentswhere the adherence metric value represents a probability or percentage,the uplift metric value associated with a potential therapy regimen fora patient of interest may be scaled or otherwise multiplied by theadherence metric value associated with that therapy regimen for thatpatient of interest to obtain a probable uplift value for the patientthat represents the likely benefit once adherence is accounted for. Inone embodiment, the recommended therapy may be selected as the therapyregimen having the highest ratio of probable uplift value to cost (e.g.,the product of the uplift metric value and adherence probability dividedby estimated cost). A GUI display may be generated or otherwise providedat the client device 106, 602 that indicates the recommended therapy tothe patient or other user of the client device 106, 602. In oneembodiment, the GUI display may include a list of potential therapiesthat are sorted, prioritize, or otherwise ordered in a manner that isinfluenced by the adherence metric values, such that the most highlyprioritized therapy corresponds to the therapy regimen recommended basedon the adherence metric.

Referring to FIGS. 17-19, it should be noted that in some embodiments,the processes 1700, 1800, 1900 may be implemented in connection with thepatient data management system 100 of FIG. 1 and adapted to leverage thegraph data structures in the database 104 to improve the accuracy of themodeling. In this regard, weighted directional or causal links betweennodes or entities may be utilized to identify predictive relationshipsand corresponding influences on patient outcomes for improved modeling.Additionally, shared links within or across logical database layers maybe utilized to identify commonalities between patients that may nototherwise be readily identifiable using conventional databases relianton tables that lack causal and/or probabilistic relationships betweenentities.

Infusion System Integration

FIG. 20 depicts one exemplary embodiment of an infusion system 2000suitable for use with the subject matter described above. For example, acomputer 2008 (e.g., computing device 102) may communicate with and/orobtain data from various client electronic devices (e.g., electronicdevices 106), such as a fluid infusion device (or infusion pump) 2002(e.g., infusion device 602), a sensing arrangement 2004 (e.g., glucosesensing arrangement 604), and a command control device (CCD) 2006. Thecomponents of an infusion system 2000 may be realized using differentplatforms, designs, and configurations, and the embodiment shown in FIG.20 is not exhaustive or limiting. In practice, the infusion device 2002and the sensing arrangement 2004 are secured at desired locations on thebody of a user (or patient), as illustrated in FIG. 20. In this regard,the locations at which the infusion device 2002 and the sensingarrangement 2004 are secured to the body of the user in FIG. 20 areprovided only as a representative, non-limiting, example. The elementsof the infusion system 2000 may be similar to those described in U.S.Pat. No. 8,674,288, the subject matter of which is hereby incorporatedby reference in its entirety.

In the illustrated embodiment of FIG. 20, the infusion device 2002 isdesigned as a portable medical device suitable for infusing a fluid, aliquid, a gel, or other medicament into the body of a user. In exemplaryembodiments, the infused fluid is insulin, although many other fluidsmay be administered through infusion such as, but not limited to, HIVdrugs, drugs to treat pulmonary hypertension, iron chelation drugs, painmedications, anti-cancer treatments, medications, vitamins, hormones, orthe like. In some embodiments, the fluid may include a nutritionalsupplement, a dye, a tracing medium, a saline medium, a hydrationmedium, or the like.

The sensing arrangement 2004 generally represents the components of theinfusion system 2000 configured to sense, detect, measure or otherwisequantify a condition of the user, and may include a sensor, a monitor,or the like, for providing data indicative of the condition that issensed, detected, measured or otherwise monitored by the sensingarrangement. In this regard, the sensing arrangement 2004 may includeelectronics and enzymes reactive to a biological condition, such as ablood glucose level, or the like, of the user, and provide dataindicative of the blood glucose level to the infusion device 2002, theCCD 2006 and/or the computer 2008. For example, the infusion device2002, the CCD 2006 and/or the computer 2008 may include a display forpresenting information or data to the user based on the sensor datareceived from the sensing arrangement 2004, such as, for example, acurrent glucose level of the user, a graph or chart of the user'sglucose level versus time, device status indicators, alert messages, orthe like. In other embodiments, the infusion device 2002, the CCD 2006and/or the computer 2008 may include electronics and software that areconfigured to analyze sensor data and operate the infusion device 2002to deliver fluid to the body of the user based on the sensor data and/orpreprogrammed delivery routines. Thus, in exemplary embodiments, one ormore of the infusion device 2002, the sensing arrangement 2004, the CCD2006, and/or the computer 2008 includes a transmitter, a receiver,and/or other transceiver electronics that allow for communication withother components of the infusion system 2000, so that the sensingarrangement 2004 may transmit sensor data or monitor data to one or moreof the infusion device 2002, the CCD 2006 and/or the computer 2008.

Still referring to FIG. 20, in various embodiments, the sensingarrangement 2004 may be secured to the body of the user or embedded inthe body of the user at a location that is remote from the location atwhich the infusion device 2002 is secured to the body of the user. Invarious other embodiments, the sensing arrangement 2004 may beincorporated within the infusion device 2002. In other embodiments, thesensing arrangement 2004 may be separate and apart from the infusiondevice 2002, and may be, for example, part of the CCD 2006. In suchembodiments, the sensing arrangement 2004 may be configured to receive abiological sample, analyte, or the like, to measure a condition of theuser.

In some embodiments, the CCD 2006 and/or the computer 2008 may includeelectronics and other components configured to perform processing,delivery routine storage, and to control the infusion device 2002 in amanner that is influenced by sensor data measured by and/or receivedfrom the sensing arrangement 2004. By including control functions in theCCD 2006 and/or the computer 2008, the infusion device 2002 may be madewith more simplified electronics. However, in other embodiments, theinfusion device 2002 may include all control functions, and may operatewithout the CCD 2006 and/or the computer 2008. In various embodiments,the CCD 2006 may be a portable electronic device. In addition, invarious embodiments, the infusion device 2002 and/or the sensingarrangement 2004 may be configured to transmit data to the CCD 2006and/or the computer 2008 for display or processing of the data by theCCD 2006 and/or the computer 2008.

In some embodiments, the CCD 2006 and/or the computer 2008 may provideinformation to the user that facilitates the user's subsequent use ofthe infusion device 2002. For example, the CCD 2006 may provideinformation to the user to allow the user to determine the rate or doseof medication to be administered into the user's body. In otherembodiments, the CCD 2006 may provide information to the infusion device2002 to autonomously control the rate or dose of medication administeredinto the body of the user. In some embodiments, the sensing arrangement2004 may be integrated into the CCD 2006. Such embodiments may allow theuser to monitor a condition by providing, for example, a sample of hisor her blood to the sensing arrangement 2004 to assess his or hercondition. In some embodiments, the sensing arrangement 2004 and the CCD2006 may be used for determining glucose levels in the blood and/or bodyfluids of the user without the use of, or necessity of, a wire or cableconnection between the infusion device 2002 and the sensing arrangement2004 and/or the CCD 2006.

In some embodiments, the sensing arrangement 2004 and/or the infusiondevice 2002 are cooperatively configured to utilize a closed-loop systemfor delivering fluid to the user. Examples of sensing devices and/orinfusion pumps utilizing closed-loop systems may be found at, but arenot limited to, the following U.S. Pat. Nos. 6,088,608, 6,119,028,6,589,229, 6,740,072, 6,827,702, 7,323,142, and 7,402,153 or UnitedStates Patent Application Publication No. 2014/0066889, all of which areincorporated herein by reference in their entirety. In such embodiments,the sensing arrangement 2004 is configured to sense or measure acondition of the user, such as, blood glucose level or the like. Theinfusion device 2002 is configured to deliver fluid in response to thecondition sensed by the sensing arrangement 2004. In turn, the sensingarrangement 2004 continues to sense or otherwise quantify a currentcondition of the user, thereby allowing the infusion device 2002 todeliver fluid continuously in response to the condition currently (ormost recently) sensed by the sensing arrangement 2004 indefinitely. Insome embodiments, the sensing arrangement 2004 and/or the infusiondevice 2002 may be configured to utilize the closed-loop system only fora portion of the day, for example only when the user is asleep or awake.

FIGS. 21-23 depict one exemplary embodiment of a fluid infusion device2100 (or alternatively, infusion pump) suitable for use in an infusionsystem, such as, for example, as infusion device 602 in the infusionsystem 600 of FIG. 6 or as infusion device 2002 in the infusion system2000 of FIG. 20. The fluid infusion device 2100 is a portable medicaldevice designed to be carried or worn by a patient (or user), and thefluid infusion device 2100 may leverage any number of conventionalfeatures, components, elements, and characteristics of existing fluidinfusion devices, such as, for example, some of the features,components, elements, and/or characteristics described in U.S. Pat. Nos.6,485,465 and 7,621,893. It should be appreciated that FIGS. 21-23depict some aspects of the infusion device 2100 in a simplified manner;in practice, the infusion device 2100 could include additional elements,features, or components that are not shown or described in detailherein.

As best illustrated in FIGS. 21-22, the illustrated embodiment of thefluid infusion device 2100 includes a housing 2102 adapted to receive afluid-containing reservoir 2105. An opening 2120 in the housing 2102accommodates a fitting 2123 (or cap) for the reservoir 2105, with thefitting 2123 being configured to mate or otherwise interface with tubing2121 of an infusion set 2125 that provides a fluid path to/from the bodyof the user. In this manner, fluid communication from the interior ofthe reservoir 2105 to the user is established via the tubing 2121. Theillustrated fluid infusion device 2100 includes a human-machineinterface (HMI) 2130 (or user interface) that includes elements 2132,2134 that can be manipulated by the user to administer a bolus of fluid(e.g., insulin), to change therapy settings, to change user preferences,to select display features, and the like. The infusion device alsoincludes a display element 2126, such as a liquid crystal display (LCD)or another suitable display element, that can be used to present varioustypes of information or data to the user, such as, without limitation:the current glucose level of the patient; the time; a graph or chart ofthe patient's glucose level versus time; device status indicators; etc.

The housing 2102 is formed from a substantially rigid material having ahollow interior 2114 adapted to allow an electronics assembly 2104, asliding member (or slide) 2106, a drive system 2108, a sensor assembly2110, and a drive system capping member 2112 to be disposed therein inaddition to the reservoir 2105, with the contents of the housing 2102being enclosed by a housing capping member 2116. The opening 2120, theslide 2106, and the drive system 2108 are coaxially aligned in an axialdirection (indicated by arrow 2118), whereby the drive system 2108facilitates linear displacement of the slide 2106 in the axial direction2118 to dispense fluid from the reservoir 2105 (after the reservoir 2105has been inserted into opening 2120), with the sensor assembly 2110being configured to measure axial forces (e.g., forces aligned with theaxial direction 2118) exerted on the sensor assembly 2110 responsive tooperating the drive system 2108 to displace the slide 2106. In variousembodiments, the sensor assembly 2110 may be utilized to detect one ormore of the following: an occlusion in a fluid path that slows,prevents, or otherwise degrades fluid delivery from the reservoir 2105to a user's body; when the reservoir 2105 is empty; when the slide 2106is properly seated with the reservoir 2105; when a fluid dose has beendelivered; when the infusion pump 2100 is subjected to shock orvibration; when the infusion pump 2100 requires maintenance.

Depending on the embodiment, the fluid-containing reservoir 2105 may berealized as a syringe, a vial, a cartridge, a bag, or the like. Incertain embodiments, the infused fluid is insulin, although many otherfluids may be administered through infusion such as, but not limited to,HIV drugs, drugs to treat pulmonary hypertension, iron chelation drugs,pain medications, anti-cancer treatments, medications, vitamins,hormones, or the like. As best illustrated in FIGS. 22-23, the reservoir2105 typically includes a reservoir barrel 2119 that contains the fluidand is concentrically and/or coaxially aligned with the slide 2106(e.g., in the axial direction 2118) when the reservoir 2105 is insertedinto the infusion pump 2100. The end of the reservoir 2105 proximate theopening 2120 may include or otherwise mate with the fitting 2123, whichsecures the reservoir 2105 in the housing 2102 and prevents displacementof the reservoir 2105 in the axial direction 2118 with respect to thehousing 2102 after the reservoir 2105 is inserted into the housing 2102.As described above, the fitting 2123 extends from (or through) theopening 2120 of the housing 2102 and mates with tubing 2121 to establishfluid communication from the interior of the reservoir 2105 (e.g.,reservoir barrel 2119) to the user via the tubing 2121 and infusion set2125. The opposing end of the reservoir 2105 proximate the slide 2106includes a plunger 2117 (or stopper) positioned to push fluid frominside the barrel 2119 of the reservoir 2105 along a fluid path throughtubing 2121 to a user. The slide 2106 is configured to mechanicallycouple or otherwise engage with the plunger 2117, thereby becomingseated with the plunger 2117 and/or reservoir 2105. Fluid is forced fromthe reservoir 2105 via tubing 2121 as the drive system 2108 is operatedto displace the slide 2106 in the axial direction 2118 toward theopening 2120 in the housing 2102.

In the illustrated embodiment of FIGS. 22-23, the drive system 2108includes a motor assembly 2107 and a drive screw 2109. The motorassembly 2107 includes a motor that is coupled to drive train componentsof the drive system 2108 that are configured to convert rotational motormotion to a translational displacement of the slide 2106 in the axialdirection 2118, and thereby engaging and displacing the plunger 2117 ofthe reservoir 2105 in the axial direction 2118. In some embodiments, themotor assembly 2107 may also be powered to translate the slide 2106 inthe opposing direction (e.g., the direction opposite direction 2118) toretract and/or detach from the reservoir 2105 to allow the reservoir2105 to be replaced. In exemplary embodiments, the motor assembly 2107includes a brushless DC (BLDC) motor having one or more permanentmagnets mounted, affixed, or otherwise disposed on its rotor. However,the subject matter described herein is not necessarily limited to usewith BLDC motors, and in alternative embodiments, the motor may berealized as a solenoid motor, an AC motor, a stepper motor, apiezoelectric caterpillar drive, a shape memory actuator drive, anelectrochemical gas cell, a thermally driven gas cell, a bimetallicactuator, or the like. The drive train components may comprise one ormore lead screws, cams, ratchets, jacks, pulleys, pawls, clamps, gears,nuts, slides, bearings, levers, beams, stoppers, plungers, sliders,brackets, guides, bearings, supports, bellows, caps, diaphragms, bags,heaters, or the like. In this regard, although the illustratedembodiment of the infusion pump utilizes a coaxially aligned drivetrain, the motor could be arranged in an offset or otherwise non-coaxialmanner, relative to the longitudinal axis of the reservoir 2105.

As best shown in FIG. 23, the drive screw 2109 mates with threads 2302internal to the slide 2106. When the motor assembly 2107 is powered andoperated, the drive screw 2109 rotates, and the slide 2106 is forced totranslate in the axial direction 2118. In an exemplary embodiment, theinfusion pump 2100 includes a sleeve 2111 to prevent the slide 2106 fromrotating when the drive screw 2109 of the drive system 2108 rotates.Thus, rotation of the drive screw 2109 causes the slide 2106 to extendor retract relative to the drive motor assembly 2107. When the fluidinfusion device is assembled and operational, the slide 2106 contactsthe plunger 2117 to engage the reservoir 2105 and control delivery offluid from the infusion pump 2100. In an exemplary embodiment, theshoulder portion 2115 of the slide 2106 contacts or otherwise engagesthe plunger 2117 to displace the plunger 2117 in the axial direction2118. In alternative embodiments, the slide 2106 may include a threadedtip 2113 capable of being detachably engaged with internal threads 2304on the plunger 2117 of the reservoir 2105, as described in detail inU.S. Pat. Nos. 6,248,093 and 6,485,465, which are incorporated byreference herein.

As illustrated in FIG. 22, the electronics assembly 2104 includescontrol electronics 2124 coupled to the display element 2126, with thehousing 2102 including a transparent window portion 2128 that is alignedwith the display element 2126 to allow the display 2126 to be viewed bythe user when the electronics assembly 2104 is disposed within theinterior 2114 of the housing 2102. The control electronics 2124generally represent the hardware, firmware, processing logic and/orsoftware (or combinations thereof) configured to control operation ofthe motor assembly 2107 and/or drive system 2108. Whether suchfunctionality is implemented as hardware, firmware, a state machine, orsoftware depends upon the particular application and design constraintsimposed on the embodiment. Those familiar with the concepts describedhere may implement such functionality in a suitable manner for eachparticular application, but such implementation decisions should not beinterpreted as being restrictive or limiting. In an exemplaryembodiment, the control electronics 2124 includes one or moreprogrammable controllers that may be programmed to control operation ofthe infusion pump 2100.

The motor assembly 2107 includes one or more electrical leads 2136adapted to be electrically coupled to the electronics assembly 2104 toestablish communication between the control electronics 2124 and themotor assembly 2107. In response to command signals from the controlelectronics 2124 that operate a motor driver (e.g., a power converter)to regulate the amount of power supplied to the motor from a powersupply, the motor actuates the drive train components of the drivesystem 2108 to displace the slide 2106 in the axial direction 2118 toforce fluid from the reservoir 2105 along a fluid path (including tubing2121 and an infusion set), thereby administering doses of the fluidcontained in the reservoir 2105 into the user's body. Preferably, thepower supply is realized one or more batteries contained within thehousing 2102. Alternatively, the power supply may be a solar panel,capacitor, AC or DC power supplied through a power cord, or the like. Insome embodiments, the control electronics 2124 may operate the motor ofthe motor assembly 2107 and/or drive system 2108 in a stepwise manner,typically on an intermittent basis; to administer discrete precise dosesof the fluid to the user according to programmed delivery profiles.

Referring to FIGS. 21-23, as described above, the user interface 2130includes HMI elements, such as buttons 2132 and a directional pad 2134,that are formed on a graphic keypad overlay 2131 that overlies a keypadassembly 2133, which includes features corresponding to the buttons2132, directional pad 2134 or other user interface items indicated bythe graphic keypad overlay 2131. When assembled, the keypad assembly2133 is coupled to the control electronics 2124, thereby allowing theHMI elements 2132, 2134 to be manipulated by the user to interact withthe control electronics 2124 and control operation of the infusion pump2100, for example, to administer a bolus of insulin, to change therapysettings, to change user preferences, to select display features, to setor disable alarms and reminders, and the like. In this regard, thecontrol electronics 2124 maintains and/or provides information to thedisplay 2126 regarding program parameters, delivery profiles, pumpoperation, alarms, warnings, statuses, or the like, which may beadjusted using the HMI elements 2132, 2134. In various embodiments, theHMI elements 2132, 2134 may be realized as physical objects (e.g.,buttons, knobs, joysticks, and the like) or virtual objects (e.g., usingtouch-sensing and/or proximity-sensing technologies). For example, insome embodiments, the display 2126 may be realized as a touch screen ortouch-sensitive display, and in such embodiments, the features and/orfunctionality of the HMI elements 2132, 2134 may be integrated into thedisplay 2126 and the HMI 2130 may not be present. In some embodiments,the electronics assembly 2104 may also include alert generating elementscoupled to the control electronics 2124 and suitably configured togenerate one or more types of feedback, such as, without limitation:audible feedback; visual feedback; haptic (physical) feedback; or thelike.

Referring to FIGS. 22-23, in accordance with one or more embodiments,the sensor assembly 2110 includes a back plate structure 2150 and aloading element 2160. The loading element 2160 is disposed between thecapping member 2112 and a beam structure 2170 that includes one or morebeams having sensing elements disposed thereon that are influenced bycompressive force applied to the sensor assembly 2110 that deflects theone or more beams, as described in greater detail in U.S. Pat. No.8,474,332, which is incorporated by reference herein. In exemplaryembodiments, the back plate structure 2150 is affixed, adhered, mounted,or otherwise mechanically coupled to the bottom surface 2138 of thedrive system 2108 such that the back plate structure 2150 residesbetween the bottom surface 2138 of the drive system 2108 and the housingcap 2116. The drive system capping member 2112 is contoured toaccommodate and conform to the bottom of the sensor assembly 2110 andthe drive system 2108. The drive system capping member 2112 may beaffixed to the interior of the housing 2102 to prevent displacement ofthe sensor assembly 2110 in the direction opposite the direction offorce provided by the drive system 2108 (e.g., the direction oppositedirection 2118). Thus, the sensor assembly 2110 is positioned betweenthe motor assembly 2107 and secured by the capping member 2112, whichprevents displacement of the sensor assembly 2110 in a downwarddirection opposite the direction of arrow 2118, such that the sensorassembly 2110 is subjected to a reactionary compressive force when thedrive system 2108 and/or motor assembly 2107 is operated to displace theslide 2106 in the axial direction 2118 in opposition to the fluidpressure in the reservoir 2105. Under normal operating conditions, thecompressive force applied to the sensor assembly 2110 is correlated withthe fluid pressure in the reservoir 2105. As shown, electrical leads2140 are adapted to electrically couple the sensing elements of thesensor assembly 2110 to the electronics assembly 2104 to establishcommunication to the control electronics 2124, wherein the controlelectronics 2124 are configured to measure, receive, or otherwise obtainelectrical signals from the sensing elements of the sensor assembly 2110that are indicative of the force applied by the drive system 2108 in theaxial direction 2118.

FIG. 24 depicts an exemplary embodiment of a patient monitoring system2400 suitable for use with the subject matter described herein. Thepatient monitoring system 2400 includes a medical device 2402 that iscommunicatively coupled to a sensing element 2404 that is inserted intothe body of a patient or otherwise worn by the patient to obtainmeasurement data indicative of a physiological condition in the body ofthe patient, such as a sensed glucose level. The medical device 2402 iscommunicatively coupled to a client device 2406 via a communicationsnetwork 2410, with the client device 2406 being communicatively coupledto a remote device 2414 via another communications network 2412. In thisregard, the client device 2406 may function as an intermediary foruploading or otherwise providing measurement data from the medicaldevice 2402 to the remote device 2414 (e.g., server 102). It should beappreciated that FIG. 24 depicts a simplified representation of apatient monitoring system 2400 for purposes of explanation and is notintended to limit the subject matter described herein in any way.

In exemplary embodiments, the client device 2406 is realized as a mobilephone, a smartphone, a tablet computer, or other similar mobileelectronic device; however, in other embodiments, the client device 2406may be realized as any sort of electronic device capable ofcommunicating with the medical device 2402 via network 2410, such as alaptop or notebook computer, a desktop computer, or the like. Inexemplary embodiments, the network 2410 is realized as a Bluetoothnetwork, a ZigBee network, or another suitable personal area network.That said, in other embodiments, the network 2410 could be realized as awireless ad hoc network, a wireless local area network (WLAN), or localarea network (LAN). The client device 2406 includes or is coupled to adisplay device, such as a monitor, screen, or another conventionalelectronic display, capable of graphically presenting data and/orinformation pertaining to the physiological condition of the patient.The client device 2406 also includes or is otherwise associated with auser input device, such as a keyboard, a mouse, a touchscreen, or thelike, capable of receiving input data and/or other information from theuser of the client device 2406.

In exemplary embodiments, a user, such as the patient, the patient'sdoctor or another healthcare provider, or the like, manipulates theclient device 2406 to execute a client application 2408 that supportscommunicating with the medical device 2402 via the network 2410. In thisregard, the client application 2408 supports establishing acommunications session with the medical device 2402 on the network 2410and receiving data and/or information from the medical device 2402 viathe communications session. The medical device 2402 may similarlyexecute or otherwise implement a corresponding application or processthat supports establishing the communications session with the clientapplication 2408. The client application 2408 generally represents asoftware module or another feature that is generated or otherwiseimplemented by the client device 2406 to support the processes describedherein. Accordingly, the client device 2406 generally includes aprocessing system and a data storage element (or memory) capable ofstoring programming instructions for execution by the processing system,that, when read and executed, cause processing system to create,generate, or otherwise facilitate the client application 2408 andperform or otherwise support the processes, tasks, operations, and/orfunctions described herein. Depending on the embodiment, the processingsystem may be implemented using any suitable processing system and/ordevice, such as, for example, one or more processors, central processingunits (CPUs), controllers, microprocessors, microcontrollers, processingcores and/or other hardware computing resources configured to supportthe operation of the processing system described herein. Similarly, thedata storage element or memory may be realized as a random access memory(RAM), read only memory (ROM), flash memory, magnetic or optical massstorage, or any other suitable non-transitory short or long term datastorage or other computer-readable media, and/or any suitablecombination thereof.

In one or more embodiments, the client device 2406 and the medicaldevice 2402 establish an association (or pairing) with one another overthe network 2410 to support subsequently establishing a point-to-pointor peer-to-peer communications session between the medical device 2402and the client device 2406 via the network 2410. For example, inaccordance with one embodiment, the network 2410 is realized as aBluetooth network, wherein the medical device 2402 and the client device2406 are paired with one another (e.g., by obtaining and storing networkidentification information for one another) by performing a discoveryprocedure or another suitable pairing procedure. The pairing informationobtained during the discovery procedure allows either of the medicaldevice 2402 or the client device 2406 to initiate the establishment of asecure communications session via the network 2410.

In one or more exemplary embodiments, the client application 2408 isalso configured to store or otherwise maintain an address and/or otheridentification information for the remote device 2414 on the secondnetwork 2412. In this regard, the second network 2412 may be physicallyand/or logically distinct from the network 2410, such as, for example,the Internet, a cellular network, a wide area network (WAN), or thelike. The remote device 2414 generally represents a server or othercomputing device configured to receive and analyze or otherwise monitormeasurement data, event log data, and potentially other informationobtained for the patient associated with the medical device 2402. Inexemplary embodiments, the remote device 2414 is coupled to a database2416 (e.g., database 104) configured to store or otherwise maintain dataassociated with individual patients. In practice, the remote device 2414may reside at a location that is physically distinct and/or separatefrom the medical device 2402 and the client device 2406, such as, forexample, at a facility that is owned and/or operated by or otherwiseaffiliated with a manufacturer of the medical device 2402. For purposesof explanation, but without limitation, the remote device 2414 mayalternatively be referred to herein as a server.

Still referring to FIG. 24, the sensing element 2404 generallyrepresents the component of the patient monitoring system 2400 that isconfigured to generate, produce, or otherwise output one or moreelectrical signals indicative of a physiological condition that issensed, measured, or otherwise quantified by the sensing element 2404.In this regard, the physiological condition of a user influences acharacteristic of the electrical signal output by the sensing element2404, such that the characteristic of the output signal corresponds toor is otherwise correlative to the physiological condition that thesensing element 2404 is sensitive to. In exemplary embodiments, thesensing element 2404 is realized as an interstitial glucose sensingelement inserted at a location on the body of the patient that generatesan output electrical signal having a current (or voltage) associatedtherewith that is correlative to the interstitial fluid glucose levelthat is sensed or otherwise measured in the body of the patient by thesensing element 2404.

The medical device 2402 generally represents the component of thepatient monitoring system 2400 that is communicatively coupled to theoutput of the sensing element 2404 to receive or otherwise obtain themeasurement data samples from the sensing element 2404 (e.g., themeasured glucose and characteristic impedance values), store orotherwise maintain the measurement data samples, and upload or otherwisetransmit the measurement data to the server 2414 via the client device2406. In one or more embodiments, the medical device 2402 is realized asan infusion device 602, 2002 configured to deliver a fluid, such asinsulin, to the body of the patient. That said, in other embodiments,the medical device 2402 could be a standalone sensing or monitoringdevice separate and independent from an infusion device (e.g., sensingarrangement 604, 2004). It should be noted that although FIG. 24 depictsthe medical device 2402 and the sensing element 2404 as separatecomponents, in practice, the medical device 2402 and the sensing element2404 may be integrated or otherwise combined to provide a unitary devicethat can be worn by the patient.

In exemplary embodiments, the medical device 2402 includes a controlmodule 2422, a data storage element 2424 (or memory), and acommunications interface 2426. The control module 2422 generallyrepresents the hardware, circuitry, logic, firmware and/or othercomponent(s) of the medical device 2402 that is coupled to the sensingelement 2404 to receive the electrical signals output by the sensingelement 2404 and perform or otherwise support various additional tasks,operations, functions and/or processes described herein. Depending onthe embodiment, the control module 2422 may be implemented or realizedwith a general purpose processor, a microprocessor, a controller, amicrocontroller, a state machine, a content addressable memory, anapplication specific integrated circuit, a field programmable gatearray, any suitable programmable logic device, discrete gate ortransistor logic, discrete hardware components, or any combinationthereof, designed to perform the functions described herein. In someembodiments, the control module 2422 includes an analog-to-digitalconverter (ADC) or another similar sampling arrangement that samples orotherwise converts an output electrical signal received from the sensingelement 2404 into corresponding digital measurement data value. In otherembodiments, the sensing element 2404 may incorporate an ADC and outputa digital measurement value.

The communications interface 2426 generally represents the hardware,circuitry, logic, firmware and/or other components of the medical device2402 that are coupled to the control module 2422 for outputting dataand/or information from/to the medical device 2402 to/from the clientdevice 2406. For example, the communications interface 2426 may includeor otherwise be coupled to one or more transceiver modules capable ofsupporting wireless communications between the medical device 2402 andthe client device 2406. In exemplary embodiments, the communicationsinterface 2426 is realized as a Bluetooth transceiver or adapterconfigured to support Bluetooth Low Energy (BLE) communications.

In exemplary embodiments, the remote device 2414 receives, from theclient device 2406, measurement data values associated with a particularpatient (e.g., sensor glucose measurements, acceleration measurements,and the like) that were obtained using the sensing element 2404, and theremote device 2414 stores or otherwise maintains the historicalmeasurement data in the database 2416 in association with the patient(e.g., using one or more unique patient identifiers). Additionally, theremote device 2414 may also receive, from or via the client device 2406,meal data or other event log data that may be input or otherwiseprovided by the patient (e.g., via client application 2408) and store orotherwise maintain historical meal data and other historical event oractivity data associated with the patient in the database 2416. In thisregard, the meal data include, for example, a time or timestampassociated with a particular meal event, a meal type or otherinformation indicative of the content or nutritional characteristics ofthe meal, and an indication of the size associated with the meal. Inexemplary embodiments, the remote device 2414 also receives historicalfluid delivery data corresponding to basal or bolus dosages of fluiddelivered to the patient by an infusion device 602, 2002. For example,the client application 2408 may communicate with an infusion device 602,2002 to obtain insulin delivery dosage amounts and correspondingtimestamps from the infusion device 602, 2002, and then upload theinsulin delivery data to the remote device 2414 for storage inassociation with the particular patient. The remote device 2414 may alsoreceive geolocation data and potentially other contextual dataassociated with a device 2402, 2406 from the client device 2406 and/orclient application 2408, and store or otherwise maintain the historicaloperational context data in association with the particular patient. Inthis regard, one or more of the devices 2402, 2406 may include a globalpositioning system (GPS) receiver or similar modules, components orcircuitry capable of outputting or otherwise providing datacharacterizing the geographic location of the respective device 2402,2406 in real-time.

As described above, in one or more exemplary embodiments, the remotedevice 2414 utilizes machine learning to determine which combination ofvariables, fields, or attributes of the historical observational patientdata are correlated to or predictive of the occurrence of a particularevent, activity, or metric for a particular patient, and then determinesa corresponding equation, function, or model for calculating the valueof the parameter of interest based on that set of input variables. Thus,the resultant model is capable of characterizing or mapping a particularcombination of one or more of the current (or recent) sensor glucosemeasurement data, auxiliary measurement data, delivery data, geographiclocation, patient behavior or activities, and the like to a valuerepresentative of the current probability or likelihood of a particularevent or activity or a current value for a parameter of interest. Itshould be noted that since each patient's physiological response mayvary from the rest of the population, the subset of input variables thatare predictive of or correlative for a particular patient may vary fromother users when the modeling is performed on a per-patient basis.Additionally, in such embodiments, the relative weightings applied tothe respective variables of that predictive subset may also vary fromother patients who may have common predictive subsets, based ondiffering correlations between a particular input variable and thehistorical data for that particular patient. It should be noted that anynumber of different machine learning techniques may be utilized by theremote device 2414 to determine what input variables are predictive fora current patient of interest, such as, for example, artificial neuralnetworks, genetic programming, support vector machines, Bayesiannetworks, probabilistic machine learning models, or other Bayesiantechniques, fuzzy logic, heuristically derived combinations, or thelike.

Diabetes Data Management System Overview

FIG. 25 illustrates a computing device 2500 suitable for use as part ofa diabetes data management system in conjunction with one or more of theprocesses described above. The diabetes data management system (DDMS)may be referred to as the Medtronic MiniMed CARELINK™ system or as amedical data management system (MDMS) in some embodiments. The DDMS maybe housed on a server or a plurality of servers which a user or a healthcare professional may access via a communications network via theInternet or the World Wide Web. Some models of the DDMS, which isdescribed as an MDMS, are described in U.S. Patent ApplicationPublication Nos. 2006/0031094 and 2013/0338630, which is hereinincorporated by reference in their entirety.

While descriptions of embodiments are made in regard to monitoringmedical or biological conditions for subjects having diabetes, thesystems and processes herein are applicable to monitoring medical orbiological conditions for cardiac subjects, cancer subjects, HIVsubjects, subjects with other disease, infection, or controllableconditions, or various combinations thereof.

In embodiments of the invention, the DDMS may be installed in acomputing device in a health care provider's office, such as a doctor'soffice, a nurse's office, a clinic, an emergency room, an urgent careoffice. Health care providers may be reluctant to utilize a system wheretheir confidential patient data is to be stored in a computing devicesuch as a server on the Internet.

The DDMS may be installed on a computing device 2500. The computingdevice 2500 may be coupled to a display 2533. In some embodiments, thecomputing device 2500 may be in a physical device separate from thedisplay (such as in a personal computer, a mini-computer, etc.) In someembodiments, the computing device 2500 may be in a single physicalenclosure or device with the display 2533 such as a laptop where thedisplay 2533 is integrated into the computing device. In embodiments ofthe invention, the computing device 2500 hosting the DDMS may be, but isnot limited to, a desktop computer, a laptop computer, a server, anetwork computer, a personal digital assistant (PDA), a portabletelephone including computer functions, a pager with a large visibledisplay, an insulin pump including a display, a glucose sensor includinga display, a glucose meter including a display, and/or a combinationinsulin pump/glucose sensor having a display. The computing device mayalso be an insulin pump coupled to a display, a glucose meter coupled toa display, or a glucose sensor coupled to a display. The computingdevice 2500 may also be a server located on the Internet that isaccessible via a browser installed on a laptop computer, desktopcomputer, a network computer, or a PDA. The computing device 2500 mayalso be a server located in a doctor's office that is accessible via abrowser installed on a portable computing device, e.g., laptop, PDA,network computer, portable phone, which has wireless capabilities andcan communicate via one of the wireless communication protocols such asBluetooth and IEEE 802.11 protocols.

In the embodiment shown in FIG. 25, the data management system 2516comprises a group of interrelated software modules or layers thatspecialize in different tasks. The system software includes a devicecommunication layer 2524, a data parsing layer 2526, a database layer2528, database storage devices 2529, a reporting layer 2530, a graphdisplay layer 2531, and a user interface layer 2532. The diabetes datamanagement system may communicate with a plurality of subject supportdevices 2512, two of which are illustrated in FIG. 25. Although thedifferent reference numerals refer to a number of layers, (e.g., adevice communication layer, a data parsing layer, a database layer),each layer may include a single software module or a plurality ofsoftware modules. For example, the device communications layer 2524 mayinclude a number of interacting software modules, libraries, etc. Inembodiments of the invention, the data management system 2516 may beinstalled onto a non-volatile storage area (memory such as flash memory,hard disk, removable hard, DVD-RW, CD-RW) of the computing device 2500.If the data management system 2516 is selected or initiated, the system2516 may be loaded into a volatile storage (memory such as DRAM, SRAM,RAM, DDRAM) for execution.

The device communication layer 2524 is responsible for interfacing withat least one, and, in further embodiments, to a plurality of differenttypes of subject support devices 2512, such as, for example, bloodglucose meters, glucose sensors/monitors, or an infusion pump. In oneembodiment, the device communication layer 2524 may be configured tocommunicate with a single type of subject support device 2512. However,in more comprehensive embodiments, the device communication layer 2524is configured to communicate with multiple different types of subjectsupport devices 2512, such as devices made from multiple differentmanufacturers, multiple different models from a particular manufacturerand/or multiple different devices that provide different functions (suchas infusion functions, sensing functions, metering functions,communication functions, user interface functions, or combinationsthereof). By providing an ability to interface with multiple differenttypes of subject support devices 2512, the diabetes data managementsystem 2516 may collect data from a significantly greater number ofdiscrete sources. Such embodiments may provide expanded and improveddata analysis capabilities by including a greater number of subjects andgroups of subjects in statistical or other forms of analysis that canbenefit from larger amounts of sample data and/or greater diversity insample data, and, thereby, improve capabilities of determiningappropriate treatment parameters, diagnostics, or the like.

The device communication layer 2524 allows the DDMS 2516 to receiveinformation from and transmit information to or from each subjectsupport device 2512 in the system 2516. Depending upon the embodimentand context of use, the type of information that may be communicatedbetween the system 2516 and device 2512 may include, but is not limitedto, data, programs, updated software, education materials, warningmessages, notifications, device settings, therapy parameters, or thelike. The device communication layer 2524 may include suitable routinesfor detecting the type of subject support device 2512 in communicationwith the system 2516 and implementing appropriate communicationprotocols for that type of device 2512. Alternatively or in addition,the subject support device 2512 may communicate information in packetsor other data arrangements, where the communication includes a preambleor other portion that includes device identification information foridentifying the type of the subject support device. Alternatively, or inaddition, the subject support device 2512 may include suitableuser-operable interfaces for allowing a user to enter information (e.g.,by selecting an optional icon or text or other device identifier) thatcorresponds to the type of subject support device used by that user.Such information may be communicated to the system 2516, through anetwork connection. In yet further embodiments, the system 2516 maydetect the type of subject support device 2512 it is communicating within the manner described above and then may send a message requiring theuser to verify that the system 2516 properly detected the type ofsubject support device being used by the user. For systems 2516 that arecapable of communicating with multiple different types of subjectsupport devices 2512, the device communication layer 2524 may be capableof implementing multiple different communication protocols and selects aprotocol that is appropriate for the detected type of subject supportdevice.

The data-parsing layer 2526 is responsible for validating the integrityof device data received and for inputting it correctly into a database2529. A cyclic redundancy check CRC process for checking the integrityof the received data may be employed. Alternatively, or in addition,data may be received in packets or other data arrangements, wherepreambles or other portions of the data include device typeidentification information. Such preambles or other portions of thereceived data may further include device serial numbers or otheridentification information that may be used for validating theauthenticity of the received information. In such embodiments, thesystem 2516 may compare received identification information withpre-stored information to evaluate whether the received information isfrom a valid source.

The database layer 2528 may include a centralized database repositorythat is responsible for warehousing and archiving stored data in anorganized format for later access, and retrieval. The database layer2528 operates with one or more data storage device(s) 2529 suitable forstoring and providing access to data in the manner described herein.Such data storage device(s) 2529 may comprise, for example, one or morehard discs, optical discs, tapes, digital libraries or other suitabledigital or analog storage media and associated drive devices, drivearrays or the like.

Data may be stored and archived for various purposes, depending upon theembodiment and environment of use. Information regarding specificsubjects and patient support devices may be stored and archived and madeavailable to those specific subjects, their authorized healthcareproviders and/or authorized healthcare payor entities for analyzing thesubject's condition. Also, certain information regarding groups ofsubjects or groups of subject support devices may be made available moregenerally for healthcare providers, subjects, personnel of the entityadministering the system 2516 or other entities, for analyzing groupdata or other forms of conglomerate data.

Embodiments of the database layer 2528 and other components of thesystem 2516 may employ suitable data security measures for securingpersonal medical information of subjects, while also allowingnon-personal medical information to be more generally available foranalysis. Embodiments may be configured for compliance with suitablegovernment regulations, industry standards, policies or the like,including, but not limited to the Health Insurance Portability andAccountability Act of 1996 (HIPAA).

The database layer 2528 may be configured to limit access of each userto types of information pre-authorized for that user. For example, asubject may be allowed access to his or her individual medicalinformation (with individual identifiers) stored by the database layer2528, but not allowed access to other subject's individual medicalinformation (with individual identifiers). Similarly, a subject'sauthorized healthcare provider or payor entity may be provided access tosome or all of the subject's individual medical information (withindividual identifiers) stored by the database layer 2528, but notallowed access to another individual's personal information. Also, anoperator or administrator-user (on a separate computer communicatingwith the computing device 2500) may be provided access to some or allsubject information, depending upon the role of the operator oradministrator. On the other hand, a subject, healthcare provider,operator, administrator or other entity, may be authorized to accessgeneral information of unidentified individuals, groups or conglomerates(without individual identifiers) stored by the database layer 2528 inthe data storage devices 2529.

In exemplary embodiments, the database 2529 stores uploaded measurementdata for a patient (e.g., sensor glucose measurement and characteristicimpedance values) along with event log data consisting of event recordscreated during a monitoring period corresponding to the measurementdata. In embodiments of the invention, the database layer 2528 may alsostore preference profiles. In the database layer 2528, for example, eachuser may store information regarding specific parameters that correspondto the user. Illustratively, these parameters could include target bloodglucose or sensor glucose levels, what type of equipment the usersutilize (insulin pump, glucose sensor, blood glucose meter, etc.) andcould be stored in a record, a file, or a memory location in the datastorage device(s) 2529 in the database layer. Preference profiles mayinclude various threshold values, monitoring period values,prioritization criteria, filtering criteria, and/or other user-specificvalues for parameters to generate a snapshot GUI display on the display2533 or a support device 2512 in a personalized or patient-specificmanner.

The DDMS 2516 may measure, analyze, and track either blood glucose (BG)or sensor glucose (SG) measurements (or readings) for a user. Inembodiments of the invention, the medical data management system maymeasure, track, or analyze both BG and SG readings for the user.Accordingly, although certain reports may mention or illustrate BG or SGonly, the reports may monitor and display results for the other one ofthe glucose readings or for both of the glucose readings.

The reporting layer 2530 may include a report wizard program that pullsdata from selected locations in the database 2529 and generates reportinformation from the desired parameters of interest. The reporting layer2530 may be configured to generate multiple different types of reports,each having different information and/or showing information indifferent formats (arrangements or styles), where the type of report maybe selectable by the user. A plurality of pre-set types of report (withpre-defined types of content and format) may be available and selectableby a user. At least some of the pre-set types of reports may be common,industry standard report types with which many healthcare providersshould be familiar. In exemplary embodiments described herein, thereporting layer 2530 also facilitates generation of a snapshot reportincluding a snapshot GUI display.

In embodiments of the invention, the database layer 2528 may calculatevalues for various medical information that is to be displayed on thereports generated by the report or reporting layer 2530. For example,the database layer 2528, may calculate average blood glucose or sensorglucose readings for specified timeframes. In embodiments of theinvention, the reporting layer 2530 may calculate values for medical orphysical information that is to be displayed on the reports. Forexample, a user may select parameters which are then utilized by thereporting layer 2530 to generate medical information valuescorresponding to the selected parameters. In other embodiments of theinvention, the user may select a parameter profile that previouslyexisted in the database layer 2528.

Alternatively, or in addition, the report wizard may allow a user todesign a custom type of report. For example, the report wizard may allowa user to define and input parameters (such as parameters specifying thetype of content data, the time period of such data, the format of thereport, or the like) and may select data from the database and arrangethe data in a printable or displayable arrangement, based on theuser-defined parameters. In further embodiments, the report wizard mayinterface with or provide data for use by other programs that may beavailable to users, such as common report generating, formatting orstatistical analysis programs. In this manner, users may import datafrom the system 2516 into further reporting tools familiar to the user.The reporting layer 2530 may generate reports in displayable form toallow a user to view reports on a standard display device, printableform to allow a user to print reports on standard printers, or othersuitable forms for access by a user. Embodiments may operate withconventional file format schemes for simplifying storing, printing andtransmitting functions, including, but not limited to PDF, JPEG, or thelike. Illustratively, a user may select a type of report and parametersfor the report and the reporting layer 2530 may create the report in aPDF format. A PDF plug-in may be initiated to help create the report andalso to allow the user to view the report. Under these operatingconditions, the user may print the report utilizing the PDF plug-in. Incertain embodiments in which security measures are implemented, forexample, to meet government regulations, industry standards or policiesthat restrict communication of subject's personal information, some orall reports may be generated in a form (or with suitable softwarecontrols) to inhibit printing, or electronic transfer (such as anon-printable and/or non-capable format). In yet further embodiments,the system 2516 may allow a user generating a report to designate thereport as non-printable and/or non-transferable, whereby the system 2516will provide the report in a form that inhibits printing and/orelectronic transfer.

The reporting layer 2530 may transfer selected reports to the graphdisplay layer 2531. The graph display layer 2531 receives informationregarding the selected reports and converts the data into a format thatcan be displayed or shown on a display 2533.

In embodiments of the invention, the reporting layer 2530 may store anumber of the user's parameters. Illustratively, the reporting layer2530 may store the type of carbohydrate units, a blood glucose movementor sensor glucose reading, a carbohydrate conversion factor, andtimeframes for specific types of reports. These examples are meant to beillustrative and not limiting.

Data analysis and presentations of the reported information may beemployed to develop and support diagnostic and therapeutic parameters.Where information on the report relates to an individual subject, thediagnostic and therapeutic parameters may be used to assess the healthstatus and relative well-being of that subject, assess the subject'scompliance to a therapy, as well as to develop or modify treatment forthe subject and assess the subject's behaviors that affect his/hertherapy. Where information on the report relates to groups of subjectsor conglomerates of data, the diagnostic and therapeutic parameters maybe used to assess the health status and relative well-being of groups ofsubjects with similar medical conditions, such as, but not limited to,diabetic subjects, cardiac subjects, diabetic subjects having aparticular type of diabetes or cardiac condition, subjects of aparticular age, sex or other demographic group, subjects with conditionsthat influence therapeutic decisions such as but not limited topregnancy, obesity, hypoglycemic unawareness, learning disorders,limited ability to care for self, various levels of insulin resistance,combinations thereof, or the like.

The user interface layer 2532 supports interactions with the end user,for example, for user login and data access, software navigation, datainput, user selection of desired report types and the display ofselected information. Users may also input parameters to be utilized inthe selected reports via the user interface layer 2532. Examples ofusers include but are not limited to: healthcare providers, healthcarepayer entities, system operators or administrators, researchers,business entities, healthcare institutions and organizations, or thelike, depending upon the service being provided by the system anddepending upon the invention embodiment. More comprehensive embodimentsare capable of interacting with some or all of the above-noted types ofusers, wherein different types of users have access to differentservices or data or different levels of services or data.

In an example embodiment, the user interface layer 2532 provides one ormore websites accessible by users on the Internet. The user interfacelayer may include or operate with at least one (or multiple) suitablenetwork server(s) to provide the website(s) over the Internet and toallow access, world-wide, from Internet-connected computers usingstandard Internet browser software. The website(s) may be accessed byvarious types of users, including but not limited to subjects,healthcare providers, researchers, business entities, healthcareinstitutions and organizations, payor entities, pharmaceutical partnersor other sources of pharmaceuticals or medical equipment, and/or supportpersonnel or other personnel running the system 2516, depending upon theembodiment of use.

In another example embodiment, where the DDMS 2516 is located on onecomputing device 2500, the user interface layer 2532 provides a numberof menus to the user to navigate through the DDMS. These menus may becreated utilizing any menu format, including but not limited to HTML,XML, or Active Server pages. A user may access the DDMS 2516 to performone or more of a variety of tasks, such as accessing general informationmade available on a website to all subjects or groups of subjects. Theuser interface layer 2532 of the DDMS 2516 may allow a user to accessspecific information or to generate reports regarding that subject'smedical condition or that subject's medical device(s) 2512, to transferdata or other information from that subject's support device(s) 2512 tothe system 2516, to transfer data, programs, program updates or otherinformation from the system 2516 to the subject's support device(s)2512, to manually enter information into the system 2516, to engage in aremote consultation exchange with a healthcare provider, or to modifythe custom settings in a subject's supported device and/or in asubject's DDMS/MDMS data file.

The system 2516 may provide access to different optional resources oractivities (including accessing different information items andservices) to different users and to different types or groups of users,such that each user may have a customized experience and/or each type orgroup of user (e.g., all users, diabetic users, cardio users, healthcareprovider-user or payor-user, or the like) may have a different set ofinformation items or services available on the system. The system 2516may include or employ one or more suitable resource provisioning programor system for allocating appropriate resources to each user or type ofuser, based on a pre-defined authorization plan. Resource provisioningsystems are well known in connection with provisioning of electronicoffice resources (email, software programs under license, sensitivedata, etc.) in an office environment, for example, in a local areanetwork LAN for an office, company or firm. In one example embodiment,such resource provisioning systems is adapted to control access tomedical information and services on the DDMS 2516, based on the type ofuser and/or the identity of the user.

Upon entering successful verification of the user's identificationinformation and password, the user may be provided access to secure,personalized information stored on the DDMS 2516. For example, the usermay be provided access to a secure, personalized location in the DDMS2516 which has been assigned to the subject. This personalized locationmay be referred to as a personalized screen, a home screen, a home menu,a personalized page, etc. The personalized location may provide apersonalized home screen to the subject, including selectable icons ormenu items for selecting optional activities, including, for example, anoption to transfer device data from a subject's supported device 2512 tothe system 2516, manually enter additional data into the system 2516,modify the subject's custom settings, and/or view and print reports.Reports may include data specific to the subject's condition, includingbut not limited to, data obtained from the subject's subject supportdevice(s) 2512, data manually entered, data from medical libraries orother networked therapy management systems, data from the subjects orgroups of subjects, or the like. Where the reports includesubject-specific information and subject identification information, thereports may be generated from some or all subject data stored in asecure storage area (e.g., storage devices 2529) employed by thedatabase layer 2528.

The user may select an option to transfer (send) device data to themedical data management system 2516. If the system 2516 receives auser's request to transfer device data to the system, the system 2516may provide the user with step-by-step instructions on how to transferdata from the subject's supported device(s) 2512. For example, the DDMS2516 may have a plurality of different stored instruction sets forinstructing users how to download data from different types of subjectsupport devices, where each instruction set relates to a particular typeof subject supported device (e.g., pump, sensor, meter, or the like), aparticular manufacturer's version of a type of subject support device,or the like. Registration information received from the user duringregistration may include information regarding the type of subjectsupport device(s) 2512 used by the subject. The system 2516 employs thatinformation to select the stored instruction set(s) associated with theparticular subject's support device(s) 2512 for display to the user.

Other activities or resources available to the user on the system 2516may include an option for manually entering information to the DDMS/MDMS2516. For example, from the user's personalized menu or location, theuser may select an option to manually enter additional information intothe system 2516.

Further optional activities or resources may be available to the user onthe DDMS 2516. For example, from the user's personalized menu, the usermay select an option to receive data, software, software updates,treatment recommendations or other information from the system 2516 onthe subject's support device(s) 2512. If the system 2516 receives arequest from a user to receive data, software, software updates,treatment recommendations or other information, the system 2516 mayprovide the user with a list or other arrangement of multiple selectableicons or other indicia representing available data, software, softwareupdates or other information available to the user.

Yet further optional activities or resources may be available to theuser on the medical data management system 2516 including, for example,an option for the user to customize or otherwise further personalize theuser's personalized location or menu. In particular, from the user'spersonalized location, the user may select an option to customizeparameters for the user. In addition, the user may create profiles ofcustomizable parameters. When the system 2516 receives such a requestfrom a user, the system 2516 may provide the user with a list or otherarrangement of multiple selectable icons or other indicia representingparameters that may be modified to accommodate the user's preferences.When a user selects one or more of the icons or other indicia, thesystem 2516 may receive the user's request and makes the requestedmodification.

In one or more exemplary embodiments, for an individual patient in theDDMS, the computing device 2500 of the DDMS is configured to analyzethat patient's historical measurement data, historical delivery data,historical event log data, and any other historical or contextual dataassociated with the patient maintained in the database layer 2528 tosupport one or more of the processes described herein. In this regard,machine learning, artificial intelligence, or similar mathematicalmodeling of the patient's physiological behavior or response may beperformed at the computing device 2500 to facilitate patient-specificcorrelations or predictions. Current measurement data, delivery data,and event log data associated with the patient along with currentcontextual data may be analyzed using the resultant models, either atthe computing device 2500 of the DDMS or another device 2512 todetermine predictions or other probable events, behaviors, or outcomespertaining to a patient in real-time.

For the sake of brevity, conventional techniques related to glucosesensing and/or monitoring, sensor calibration and/or compensation,bolusing, machine learning and/or artificial intelligence, pharmodynamicmodeling, and other functional aspects of the subject matter may not bedescribed in detail herein. In addition, certain terminology may also beused in the herein for the purpose of reference only, and thus is notintended to be limiting. For example, terms such as “first,” “second,”and other such numerical terms referring to structures do not imply asequence or order unless clearly indicated by the context. The foregoingdescription may also refer to elements or nodes or features being“connected” or “coupled” together. As used herein, unless expresslystated otherwise, “coupled” means that one element/node/feature isdirectly or indirectly joined to (or directly or indirectly communicateswith) another element/node/feature, and not necessarily mechanically.

While at least one exemplary embodiment has been presented in theforegoing detailed description, it should be appreciated that a vastnumber of variations exist. It should also be appreciated that theexemplary embodiment or embodiments described herein are not intended tolimit the scope, applicability, or configuration of the claimed subjectmatter in any way. For example, the subject matter described herein isnot limited to the infusion devices and related systems describedherein. Moreover, the foregoing detailed description will provide thoseskilled in the art with a convenient road map for implementing thedescribed embodiment or embodiments. It should be understood thatvarious changes can be made in the function and arrangement of elementswithout departing from the scope defined by the claims, which includesknown equivalents and foreseeable equivalents at the time of filing thispatent application. Accordingly, details of the exemplary embodiments orother limitations described above should not be read into the claimsabsent a clear intention to the contrary.

What is claimed is:
 1. A method of managing a physiological condition ofa patient, the method comprising: obtaining, by a computing device,measurement data pertaining to the physiological condition of thepatient from a sensing arrangement; obtaining, by the computing device,medical record data associated with the patient from a database;obtaining, by the computing device, a plurality of different adherencemodels associated with a plurality of different therapy regimens,wherein each adherence model of the plurality of different adherencemodels corresponds to a respective therapy regimen of a plurality ofdifferent therapy regimens; determining, by the computing device, aplurality of adherence metric values associated with the patient for theplurality of different therapy regimens based on the measurement dataand the medical record data using the plurality of different adherencemodels; and providing, by the computing device, an indication of arecommended therapy regimen for the patient based at least in part on arespective adherence metric value associated with the recommendedtherapy regimen.
 2. The method of claim 1, further comprising:determining, by the computing device, a plurality of uplift metricvalues for the plurality of different therapy regimens based at least inpart on the measurement data and the medical record data; and selectingthe recommended therapy regimen from among the plurality of differenttherapy regimens based at least in part on the respective adherencemetric value and a respective uplift metric value of the plurality ofuplift metric values associated with the recommended therapy regimen. 3.The method of claim 1, further comprising obtaining contextualmeasurement data from a second sensing arrangement of the computingdevice, wherein determining the plurality of adherence metric valuescomprises determining the plurality of adherence metric values in amanner that is influenced by the contextual measurement data.
 4. Themethod of claim 1, further comprising: obtaining population measurementdata associated with a plurality of patients from the database;obtaining population medical record data associated with the pluralityof patients from the database; obtaining population medical claims dataassociated with the plurality of patients from the database; and foreach therapy regimen of the plurality of different therapy regimens:identifying a subset of the plurality of patients prescribed therespective therapy regimen based on the population medical record data;and determining the respective adherence model associated with therespective therapy regimen based on relationships between respectivesubsets of the population medical claims data, the populationmeasurement data and the population medical record data associated withthe subset of the plurality of patients.
 5. The method of claim 1,further comprising determining a risk score associated with the patientfor a medical condition based at least in part on the measurement dataand the medical record data, wherein providing the indication comprisesdisplaying the indication of the recommended therapy regimen on adisplay device associated with the computing device when the risk scoreis greater than a threshold.
 6. The method of claim 1, wherein providingthe indication of the recommended therapy regimen comprise displaying alist of the plurality of different therapy regimens ordered by therespective adherence metric values associated therewith.
 7. The methodof claim 1, further comprising selecting the recommended therapy regimenfrom among the plurality of different therapy regimens based at least inpart on the respective adherence metric value relative to remainingadherence metric values of the plurality of adherence metric values. 8.The method of claim 7, wherein selecting the recommended therapy regimencomprises identifying the recommended therapy regimen as having ahighest adherence metric value of the plurality of adherence metricvalues.
 9. A computer-readable medium having instructions stored thereonthat are executable by a processing system of the computing device toperform the method of claim
 1. 10. A method of managing a physiologicalcondition of a patient, the method comprising: obtaining, by a computingdevice, population medical record data for a plurality of patientsprescribed a therapy regimen; obtaining, by the computing device,population medical claims data for the plurality of patients; obtaining,by the computing device, population measurement data for the pluralityof patients; determining, by the computing device, an adherence modelbased at least in part on a relationship between the population medicalclaims data, the population medical record data, and the populationmeasurement data; obtaining, by the computing device, measurement datapertaining to the physiological condition of the patient from a sensingarrangement; obtaining, by the computing device, medical record dataassociated with the patient from the database; determining, by thecomputing device, an adherence metric value for the therapy regimen forthe patient based at least in part on the measurement data and themedical record data using the adherence model; and recommending, by thecomputing device, the therapy regimen for the patient based on theadherence metric value.
 11. The method of claim 10, the populationmedical claims data including prescription information pertaining to thetherapy regimen and the population medical claims data includingprescription filling information pertaining to the therapy regimen,wherein determining the adherence model comprises determining theadherence model based at least in part on one or more relationshipsbetween the prescription filling information and the prescriptioninformation.
 12. The method of claim 10, further comprising obtaining,by the computing device, population event log data for the plurality ofpatients, wherein determining the adherence model comprises determiningthe adherence model based at least in part on the population event logdata.
 13. The method of claim 12, further comprising obtaining, by thecomputing device, event log data associated with the patient from thedatabase, wherein determining the adherence metric value comprisesdetermining the adherence metric value for the therapy regimen for thepatient based at least in part on the measurement data, the medicalrecord data and the event log data using the adherence model.
 14. Themethod of claim 12, further comprising obtaining contextual measurementdata from a second sensing arrangement, wherein determining theadherence metric value comprises determining the adherence metric valuein a manner that is influenced by the contextual measurement data. 15.The method of claim 10, further comprising determining a risk scoreassociated with the patient for a medical condition based at least inpart on the measurement data and the medical record data, whereinrecommending the therapy regimen comprises displaying an indication ofthe therapy regimen on a display device associated with the computingdevice when the risk score is greater than a threshold.
 16. The methodof claim 10, further comprising determining, by the computing device, anuplift metric value for the therapy regimen for the patient based atleast in part on the measurement data and the medical record data,wherein recommending the therapy regimen is influenced by the upliftmetric value and the adherence metric value.
 17. The method of claim 10,further comprising determining, by the computing device, a cost metricvalue for the therapy regimen based at least in part on the populationmedical claims data, wherein recommending the therapy regimen isinfluenced by the cost metric value and the adherence metric value. 18.The method of claim 17, further comprising determining, by the computingdevice, an uplift metric value for the therapy regimen for the patientbased at least in part on the measurement data and the medical recorddata, wherein recommending the therapy regimen is influenced by theuplift metric value, the cost metric value, and the adherence metricvalue.
 19. A system comprising: a sensing arrangement to obtainmeasurement data pertaining to a physiological condition of a patient; adatabase to maintain medical record data associated with the patient anda plurality of different adherence models associated with a plurality ofdifferent therapy regimens, wherein each adherence model of theplurality of different adherence models corresponds to a respectivetherapy regimen of a plurality of different therapy regimens; and acomputing device communicatively coupled to the sensing arrangement andthe database to determine a plurality of adherence metric valuesassociated with the patient for the plurality of different therapyregimens based on the measurement data and the medical record data usingthe plurality of different adherence models and generate a usernotification of a recommended therapy regimen for the patient based atleast in part on a respective adherence metric value associated with therecommended therapy regimen.
 20. The system of claim 19, furthercomprising a second sensing arrangement coupled to the computing deviceto provide contextual measurement data, wherein the plurality ofadherence metric values are influenced by the contextual measurementdata.